{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e04e844f",
   "metadata": {},
   "source": [
    "# 一、导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2fffcaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import missingno\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40072b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>地址</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>公司规模1</th>\n",
       "      <th>公司规模2</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·中关村</td>\n",
       "      <td>8-12K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>安阳·文峰区·德隆东街</td>\n",
       "      <td>3-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>未融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>随州·曾都区·解放路</td>\n",
       "      <td>3-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>成都·双流区·华阳</td>\n",
       "      <td>7-10K</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>未融资</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>郑州</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>和逸科技CNABS</td>\n",
       "      <td>初级数据专员</td>\n",
       "      <td>上海·黄浦区·老西门</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，补充医疗保险，交通补助，通讯补贴，定期体检，节日福利，员工旅游，五险一金，带薪年假，餐补</td>\n",
       "      <td>互联网金融</td>\n",
       "      <td>未融资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>Excel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excel</td>\n",
       "      <td>职位描述：\\n1.\\t资产证券化产品信息的收集，与数据标准化处理并录入；\\n2.\\t独立完成...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>昆明·五华区·虹山</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>大数据</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>正沅配货网络科技...</td>\n",
       "      <td>数据维护专员</td>\n",
       "      <td>广州·番禺区·大学城</td>\n",
       "      <td>8-10K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>通讯补贴，带薪年假，节日福利，员工旅游，五险一金，年终奖，工龄奖，生日福利，绩效奖金，交通补...</td>\n",
       "      <td>互联网</td>\n",
       "      <td>未融资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>NaN</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>该岗位同时招全职人员和兼职人员\\n兼职待遇：\\n前期兼职1500一个月，要求一周能来2-3天...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>西安·雁塔区·西影路</td>\n",
       "      <td>5-10K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>徐州·云龙区·新城区</td>\n",
       "      <td>5-7K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7665 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               标题             名称           地址     薪水   经历要求 学历要求  \\\n",
       "0            东方优特          数据分析师   北京·海淀区·中关村  8-12K   经验不限   本科   \n",
       "1        松鼠Ai智能学习          数据分析师  安阳·文峰区·德隆东街   3-8K   经验不限   本科   \n",
       "2           羽山机器人          数据分析师   随州·曾都区·解放路   3-8K   经验不限   本科   \n",
       "3         麦可思数据科技      数据分析师（校招）    成都·双流区·华阳  7-10K  在校/应届   本科   \n",
       "4            天星教育  数据分析师(J10073)           郑州   6-8K   经验不限   本科   \n",
       "...           ...            ...          ...    ...    ...  ...   \n",
       "7660    和逸科技CNABS         初级数据专员   上海·黄浦区·老西门   6-8K  在校/应届   本科   \n",
       "7661           洲宇         数据分析专员    昆明·五华区·虹山   4-6K   经验不限   本科   \n",
       "7662  正沅配货网络科技...         数据维护专员   广州·番禺区·大学城  8-10K   经验不限   本科   \n",
       "7663   西安易升电子有限公司         证券数据分析   西安·雁塔区·西影路  5-10K   经验不限   本科   \n",
       "7664        江苏蕲臻堂        电商数据分析师   徐州·云龙区·新城区   5-7K   经验不限   本科   \n",
       "\n",
       "                                                     描述         工作内容   公司规模1  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶         在线教育     天使轮   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...         智能硬件     未融资   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金         人工智能   不需要融资   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...          大数据     未融资   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...        新闻/出版   不需要融资   \n",
       "...                                                 ...          ...     ...   \n",
       "7660   年终奖，补充医疗保险，交通补助，通讯补贴，定期体检，节日福利，员工旅游，五险一金，带薪年假，餐补        互联网金融     未融资   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假          大数据   0-20人   \n",
       "7662  通讯补贴，带薪年假，节日福利，员工旅游，五险一金，年终奖，工龄奖，生日福利，绩效奖金，交通补...          互联网     未融资   \n",
       "7663                                                NaN  电子/半导体/集成电路  20-99人   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游        批发/零售   不需要融资   \n",
       "\n",
       "           公司规模2        关键词1       关键词2       关键词3       关键词4        关键词5  \\\n",
       "0       100-499人        数据处理        数据库       数据挖掘        NaN         NaN   \n",
       "1          0-20人   数学/统计相关专业  数据分析/挖掘经验    智能硬件学习机         教育        全职工作   \n",
       "2          0-20人   数学/统计相关专业    计算机相关专业  数据分析/挖掘经验       教育相关         NaN   \n",
       "3       100-499人    机器学习建模经验          R        SAS  数据分析/挖掘经验      Python   \n",
       "4     1000-9999人      Python       SPSS        NaN        NaN         NaN   \n",
       "...          ...         ...        ...        ...        ...         ...   \n",
       "7660      20-99人       Excel        NaN        NaN        NaN       Excel   \n",
       "7661         NaN         SQL     Python          R  数学/统计相关专业         SQL   \n",
       "7662      20-99人   数学/统计相关专业    计算机相关专业  数据分析/挖掘经验        NaN   数学/统计相关专业   \n",
       "7663         NaN  金融行业数据相关经验     风控相关经验   证券数据归纳分析        NaN  金融行业数据相关经验   \n",
       "7664      20-99人      Python        NaN        NaN        NaN      Python   \n",
       "\n",
       "                                                     要求  \n",
       "0     岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...  \n",
       "1     1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...  \n",
       "2     岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...  \n",
       "3     我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...  \n",
       "4     工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...  \n",
       "...                                                 ...  \n",
       "7660  职位描述：\\n1.\\t资产证券化产品信息的收集，与数据标准化处理并录入；\\n2.\\t独立完成...  \n",
       "7661  1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...  \n",
       "7662  该岗位同时招全职人员和兼职人员\\n兼职待遇：\\n前期兼职1500一个月，要求一周能来2-3天...  \n",
       "7663  职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...  \n",
       "7664  岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...  \n",
       "\n",
       "[7665 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##导入数据\n",
    "file_path = \"D:/大三上/数据挖掘/课设/《大数据管理与应用专业》人才需求调研数据汇总（新）.xlsx\"  # 更新为你的文件路径\n",
    "# 读取数据\n",
    "df = pd.read_excel(file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47321773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7665 entries, 0 to 7664\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   标题      7665 non-null   object\n",
      " 1   名称      7665 non-null   object\n",
      " 2   地址      7665 non-null   object\n",
      " 3   薪水      7665 non-null   object\n",
      " 4   经历要求    7665 non-null   object\n",
      " 5   学历要求    7665 non-null   object\n",
      " 6   描述      6064 non-null   object\n",
      " 7   工作内容    7615 non-null   object\n",
      " 8   公司规模1   7665 non-null   object\n",
      " 9   公司规模2   5823 non-null   object\n",
      " 10  关键词1    7542 non-null   object\n",
      " 11  关键词2    6452 non-null   object\n",
      " 12  关键词3    5406 non-null   object\n",
      " 13  关键词4    3811 non-null   object\n",
      " 14  关键词5    3599 non-null   object\n",
      " 15  要求      7108 non-null   object\n",
      "dtypes: object(16)\n",
      "memory usage: 958.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b84741f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3103"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看重复值\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38643861",
   "metadata": {},
   "outputs": [],
   "source": [
    "#重复值处理进行删除\n",
    "df.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad1ac6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看重复值\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb7266d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>地址</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>公司规模1</th>\n",
       "      <th>公司规模2</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·中关村</td>\n",
       "      <td>8-12K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>安阳·文峰区·德隆东街</td>\n",
       "      <td>3-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>未融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>随州·曾都区·解放路</td>\n",
       "      <td>3-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>成都·双流区·华阳</td>\n",
       "      <td>7-10K</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>未融资</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>郑州</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>杭州超级伙伴科技</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>绍兴·柯桥区·柯桥万达广场</td>\n",
       "      <td>6-8K·13薪</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>电商/零售行业数据相关经验</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>NaN</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>吾执投资</td>\n",
       "      <td>量化数据研究员</td>\n",
       "      <td>上海·浦东新区·联洋</td>\n",
       "      <td>8-13K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，零食下午茶，带薪年假，包吃</td>\n",
       "      <td>基金</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>昆明·五华区·虹山</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>大数据</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>西安·雁塔区·西影路</td>\n",
       "      <td>5-10K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>徐州·云龙区·新城区</td>\n",
       "      <td>5-7K</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4562 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              标题             名称             地址        薪水   经历要求 学历要求  \\\n",
       "0           东方优特          数据分析师     北京·海淀区·中关村     8-12K   经验不限   本科   \n",
       "1       松鼠Ai智能学习          数据分析师    安阳·文峰区·德隆东街      3-8K   经验不限   本科   \n",
       "2          羽山机器人          数据分析师     随州·曾都区·解放路      3-8K   经验不限   本科   \n",
       "3        麦可思数据科技      数据分析师（校招）      成都·双流区·华阳     7-10K  在校/应届   本科   \n",
       "4           天星教育  数据分析师(J10073)             郑州      6-8K   经验不限   本科   \n",
       "...          ...            ...            ...       ...    ...  ...   \n",
       "7648    杭州超级伙伴科技          数据分析师  绍兴·柯桥区·柯桥万达广场  6-8K·13薪   经验不限   本科   \n",
       "7652        吾执投资        量化数据研究员     上海·浦东新区·联洋     8-13K   经验不限   本科   \n",
       "7661          洲宇         数据分析专员      昆明·五华区·虹山      4-6K   经验不限   本科   \n",
       "7663  西安易升电子有限公司         证券数据分析     西安·雁塔区·西影路     5-10K   经验不限   本科   \n",
       "7664       江苏蕲臻堂        电商数据分析师     徐州·云龙区·新城区      5-7K   经验不限   本科   \n",
       "\n",
       "                                                     描述         工作内容   公司规模1  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶         在线教育     天使轮   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...         智能硬件     未融资   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金         人工智能   不需要融资   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...          大数据     未融资   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...        新闻/出版   不需要融资   \n",
       "...                                                 ...          ...     ...   \n",
       "7648  五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖         电子商务   不需要融资   \n",
       "7652                                 五险一金，零食下午茶，带薪年假，包吃           基金   0-20人   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假          大数据   0-20人   \n",
       "7663                                                NaN  电子/半导体/集成电路  20-99人   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游        批发/零售   不需要融资   \n",
       "\n",
       "           公司规模2        关键词1           关键词2       关键词3       关键词4        关键词5  \\\n",
       "0       100-499人        数据处理            数据库       数据挖掘        NaN         NaN   \n",
       "1          0-20人   数学/统计相关专业      数据分析/挖掘经验    智能硬件学习机         教育        全职工作   \n",
       "2          0-20人   数学/统计相关专业        计算机相关专业  数据分析/挖掘经验       教育相关         NaN   \n",
       "3       100-499人    机器学习建模经验              R        SAS  数据分析/挖掘经验      Python   \n",
       "4     1000-9999人      Python           SPSS        NaN        NaN         NaN   \n",
       "...          ...         ...            ...        ...        ...         ...   \n",
       "7648    100-499人   数学/统计相关专业  电商/零售行业数据相关经验  数据分析/挖掘经验        NaN   数学/统计相关专业   \n",
       "7652         NaN      Python            NaN        NaN        NaN      Python   \n",
       "7661         NaN         SQL         Python          R  数学/统计相关专业         SQL   \n",
       "7663         NaN  金融行业数据相关经验         风控相关经验   证券数据归纳分析        NaN  金融行业数据相关经验   \n",
       "7664      20-99人      Python            NaN        NaN        NaN      Python   \n",
       "\n",
       "                                                     要求  \n",
       "0     岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...  \n",
       "1     1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...  \n",
       "2     岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...  \n",
       "3     我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...  \n",
       "4     工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...  \n",
       "...                                                 ...  \n",
       "7648  岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...  \n",
       "7652  职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...  \n",
       "7661  1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...  \n",
       "7663  职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...  \n",
       "7664  岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...  \n",
       "\n",
       "[4562 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd8bb309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 缺失值处理，这里以空字符串填充'关键词'字段的缺失值\n",
    "df[['关键词1', '关键词2', '关键词3', '关键词4', '关键词5']].fillna('', inplace=True)\n",
    "# 提取薪水范围的最小值和最大值\n",
    "df['薪水最小值'] = df['薪水'].str.extract('(\\d+)').astype(float)\n",
    "df['薪水最大值'] = df['薪水'].str.extract('(\\d+)K').astype(float)\n",
    "\n",
    "# 计算平均薪水\n",
    "# 由于薪水最大值中包含'K'，我们需要先将其转换为数值类型，即乘以1000\n",
    "df['薪水最小值'] = df['薪水最小值'] * 1000\n",
    "df['薪水最大值'] = df['薪水最大值'] * 1000\n",
    "df['薪水'] = (df['薪水最小值'] + df['薪水最大值']) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b48cc4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>地址</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>公司规模1</th>\n",
       "      <th>公司规模2</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·中关村</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>安阳·文峰区·德隆东街</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>未融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>随州·曾都区·解放路</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>成都·双流区·华阳</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>未融资</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>郑州</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>杭州超级伙伴科技</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>绍兴·柯桥区·柯桥万达广场</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>100-499人</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>电商/零售行业数据相关经验</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>NaN</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>吾执投资</td>\n",
       "      <td>量化数据研究员</td>\n",
       "      <td>上海·浦东新区·联洋</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，零食下午茶，带薪年假，包吃</td>\n",
       "      <td>基金</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>昆明·五华区·虹山</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>大数据</td>\n",
       "      <td>0-20人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>西安·雁塔区·西影路</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>徐州·云龙区·新城区</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4562 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              标题             名称             地址       薪水   经历要求 学历要求  \\\n",
       "0           东方优特          数据分析师     北京·海淀区·中关村  10000.0   经验不限   本科   \n",
       "1       松鼠Ai智能学习          数据分析师    安阳·文峰区·德隆东街   5500.0   经验不限   本科   \n",
       "2          羽山机器人          数据分析师     随州·曾都区·解放路   5500.0   经验不限   本科   \n",
       "3        麦可思数据科技      数据分析师（校招）      成都·双流区·华阳   8500.0  在校/应届   本科   \n",
       "4           天星教育  数据分析师(J10073)             郑州   7000.0   经验不限   本科   \n",
       "...          ...            ...            ...      ...    ...  ...   \n",
       "7648    杭州超级伙伴科技          数据分析师  绍兴·柯桥区·柯桥万达广场   7000.0   经验不限   本科   \n",
       "7652        吾执投资        量化数据研究员     上海·浦东新区·联洋  10500.0   经验不限   本科   \n",
       "7661          洲宇         数据分析专员      昆明·五华区·虹山   5000.0   经验不限   本科   \n",
       "7663  西安易升电子有限公司         证券数据分析     西安·雁塔区·西影路   7500.0   经验不限   本科   \n",
       "7664       江苏蕲臻堂        电商数据分析师     徐州·云龙区·新城区   6000.0   经验不限   本科   \n",
       "\n",
       "                                                     描述         工作内容   公司规模1  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶         在线教育     天使轮   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...         智能硬件     未融资   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金         人工智能   不需要融资   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...          大数据     未融资   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...        新闻/出版   不需要融资   \n",
       "...                                                 ...          ...     ...   \n",
       "7648  五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖         电子商务   不需要融资   \n",
       "7652                                 五险一金，零食下午茶，带薪年假，包吃           基金   0-20人   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假          大数据   0-20人   \n",
       "7663                                                NaN  电子/半导体/集成电路  20-99人   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游        批发/零售   不需要融资   \n",
       "\n",
       "           公司规模2        关键词1           关键词2       关键词3       关键词4        关键词5  \\\n",
       "0       100-499人        数据处理            数据库       数据挖掘        NaN         NaN   \n",
       "1          0-20人   数学/统计相关专业      数据分析/挖掘经验    智能硬件学习机         教育        全职工作   \n",
       "2          0-20人   数学/统计相关专业        计算机相关专业  数据分析/挖掘经验       教育相关         NaN   \n",
       "3       100-499人    机器学习建模经验              R        SAS  数据分析/挖掘经验      Python   \n",
       "4     1000-9999人      Python           SPSS        NaN        NaN         NaN   \n",
       "...          ...         ...            ...        ...        ...         ...   \n",
       "7648    100-499人   数学/统计相关专业  电商/零售行业数据相关经验  数据分析/挖掘经验        NaN   数学/统计相关专业   \n",
       "7652         NaN      Python            NaN        NaN        NaN      Python   \n",
       "7661         NaN         SQL         Python          R  数学/统计相关专业         SQL   \n",
       "7663         NaN  金融行业数据相关经验         风控相关经验   证券数据归纳分析        NaN  金融行业数据相关经验   \n",
       "7664      20-99人      Python            NaN        NaN        NaN      Python   \n",
       "\n",
       "                                                     要求   薪水最小值    薪水最大值  \n",
       "0     岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...  8000.0  12000.0  \n",
       "1     1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...  3000.0   8000.0  \n",
       "2     岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...  3000.0   8000.0  \n",
       "3     我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...  7000.0  10000.0  \n",
       "4     工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...  6000.0   8000.0  \n",
       "...                                                 ...     ...      ...  \n",
       "7648  岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...  6000.0   8000.0  \n",
       "7652  职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...  8000.0  13000.0  \n",
       "7661  1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...  4000.0   6000.0  \n",
       "7663  职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...  5000.0  10000.0  \n",
       "7664  岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...  5000.0   7000.0  \n",
       "\n",
       "[4562 rows x 18 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01468558",
   "metadata": {},
   "source": [
    "# 二、数据描述性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0eec3275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>薪水</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4560.000000</td>\n",
       "      <td>4560.000000</td>\n",
       "      <td>4560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7360.416667</td>\n",
       "      <td>5623.464912</td>\n",
       "      <td>9097.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1871.555780</td>\n",
       "      <td>1662.885257</td>\n",
       "      <td>2287.264512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3500.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8500.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 薪水        薪水最小值         薪水最大值\n",
       "count   4560.000000  4560.000000   4560.000000\n",
       "mean    7360.416667  5623.464912   9097.368421\n",
       "std     1871.555780  1662.885257   2287.264512\n",
       "min     3500.000000  1000.000000   6000.000000\n",
       "25%     6000.000000  4000.000000   7000.000000\n",
       "50%     7000.000000  5000.000000   9000.000000\n",
       "75%     8500.000000  7000.000000  10000.000000\n",
       "max    12000.000000  9000.000000  16000.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "583bc770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用missingno库进行可视化展示\n",
    "missingno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d3a424e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "标题          0\n",
       "名称          0\n",
       "地址          0\n",
       "薪水          2\n",
       "经历要求        0\n",
       "学历要求        0\n",
       "描述        912\n",
       "工作内容       50\n",
       "公司规模1       0\n",
       "公司规模2    1041\n",
       "关键词1       83\n",
       "关键词2      760\n",
       "关键词3     1337\n",
       "关键词4     2263\n",
       "关键词5     2225\n",
       "要求        556\n",
       "薪水最小值       2\n",
       "薪水最大值       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查是否有缺失值\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23784f67",
   "metadata": {},
   "source": [
    "## 异常值检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be9cd5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '薪水')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#异常值检测\n",
    "Q1 = df['薪水'].quantile(0.25)\n",
    "Q3 = df['薪水'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "df = df[(df['薪水'] >= lower_bound) & (df['薪水'] <= upper_bound)]\n",
    "# 创建图形和坐标轴对象\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# 绘制箱型图，设置颜色等样式\n",
    "boxprops = dict(linestyle='-', linewidth=1.5, color='blue')\n",
    "flierprops = dict(marker='o', markerfacecolor='red', markersize=5)\n",
    "whiskerprops = dict(linestyle='--', linewidth=1, color='gray')\n",
    "capprops = dict(linestyle=':', linewidth=0.8, color='gray')\n",
    "medianprops = dict(linestyle='-', linewidth=1.2, color='green')\n",
    "\n",
    "bp = ax.boxplot(df['薪水'], boxprops=boxprops, flierprops=flierprops,\n",
    "                whiskerprops=whiskerprops, capprops=capprops, medianprops=medianprops)\n",
    "\n",
    "# 添加网格线\n",
    "ax.yaxis.grid(True, linestyle='--', which='major', color='lightgray', alpha=0.5)\n",
    "\n",
    "# 设置标题和坐标轴标签\n",
    "ax.set_title('薪水数据箱型图')\n",
    "ax.set_xlabel('数据')\n",
    "ax.set_ylabel('薪水')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159ec88e",
   "metadata": {},
   "source": [
    "## 公司规模的提取合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6df0cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>地址</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "      <th>公司规模</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·中关村</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>100-499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>安阳·文峰区·德隆东街</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>随州·曾都区·解放路</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>成都·双流区·华阳</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100-499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>郑州</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1000-9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         标题             名称           地址       薪水   经历要求 学历要求  \\\n",
       "0      东方优特          数据分析师   北京·海淀区·中关村  10000.0   经验不限   本科   \n",
       "1  松鼠Ai智能学习          数据分析师  安阳·文峰区·德隆东街   5500.0   经验不限   本科   \n",
       "2     羽山机器人          数据分析师   随州·曾都区·解放路   5500.0   经验不限   本科   \n",
       "3   麦可思数据科技      数据分析师（校招）    成都·双流区·华阳   8500.0  在校/应届   本科   \n",
       "4      天星教育  数据分析师(J10073)           郑州   7000.0   经验不限   本科   \n",
       "\n",
       "                                                  描述   工作内容       关键词1  \\\n",
       "0         五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶   在线教育       数据处理   \n",
       "1  生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...   智能硬件  数学/统计相关专业   \n",
       "2                             员工旅游，包住，免费工装，团建聚餐，五险一金   人工智能  数学/统计相关专业   \n",
       "3  带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...    大数据   机器学习建模经验   \n",
       "4  零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...  新闻/出版     Python   \n",
       "\n",
       "        关键词2       关键词3       关键词4    关键词5  \\\n",
       "0        数据库       数据挖掘        NaN     NaN   \n",
       "1  数据分析/挖掘经验    智能硬件学习机         教育    全职工作   \n",
       "2    计算机相关专业  数据分析/挖掘经验       教育相关     NaN   \n",
       "3          R        SAS  数据分析/挖掘经验  Python   \n",
       "4       SPSS        NaN        NaN     NaN   \n",
       "\n",
       "                                                  要求   薪水最小值    薪水最大值  \\\n",
       "0  岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...  8000.0  12000.0   \n",
       "1  1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...  3000.0   8000.0   \n",
       "2  岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...  3000.0   8000.0   \n",
       "3  我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...  7000.0  10000.0   \n",
       "4  工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...  6000.0   8000.0   \n",
       "\n",
       "        公司规模  \n",
       "0    100-499  \n",
       "1       0-20  \n",
       "2       0-20  \n",
       "3    100-499  \n",
       "4  1000-9999  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.copy()\n",
    "# 定义一个函数来提取公司规模的人数\n",
    "def extract_scale(text):\n",
    "    if isinstance(text, str) and '人' in text:  # 检查是否为字符串并包含'人'\n",
    "        return text.split('人')[0].strip()  # 提取人数\n",
    "    return ''  # 返回空字符串\n",
    "\n",
    "# 应用函数到“公司规模1”和“公司规模2”列\n",
    "df1['公司规模1'] = df1['公司规模1'].apply(extract_scale)\n",
    "df1['公司规模2'] = df1['公司规模2'].apply(extract_scale)\n",
    "\n",
    "# 将“公司规模1”和“公司规模2”列合并成一列“公司规模”，使用更合适的合并方式，处理可能的空值情况\n",
    "df1['公司规模'] = df1['公司规模1'].astype(str).str.cat(df1['公司规模2'].astype(str), sep=', ')\n",
    "df1['公司规模'] = df1['公司规模'].str.strip().replace(',,', ',')  # 处理连续逗号情况\n",
    "df1['公司规模'] = df1['公司规模'].apply(lambda x: ', '.join([i for i in x.split(', ') if i]))\n",
    "\n",
    "# 直接添加“公司规模”列到DataFrame的最后一列\n",
    "df1['公司规模'] = df1['公司规模'].str.replace(',', '')\n",
    "df1 = pd.concat([df1.iloc[:, :-1], df1['公司规模'].to_frame()], axis=1)\n",
    "\n",
    "# 删除不需要的列\n",
    "columns_to_drop = ['公司规模1', '公司规模2']\n",
    "df1 = df1.drop(columns=columns_to_drop)\n",
    "\n",
    "# 显示修改后的DataFrame的前几行以检查结果\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae9a842c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9ba6e4ae",
   "metadata": {},
   "source": [
    "## 提取城市名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "141572a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>城市</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>安阳</td>\n",
       "      <td>文峰区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>随州</td>\n",
       "      <td>曾都区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>成都</td>\n",
       "      <td>双流区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1000-9999</td>\n",
       "      <td>郑州</td>\n",
       "      <td>郑州</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         标题             名称       薪水   经历要求 学历要求  \\\n",
       "0      东方优特          数据分析师  10000.0   经验不限   本科   \n",
       "1  松鼠Ai智能学习          数据分析师   5500.0   经验不限   本科   \n",
       "2     羽山机器人          数据分析师   5500.0   经验不限   本科   \n",
       "3   麦可思数据科技      数据分析师（校招）   8500.0  在校/应届   本科   \n",
       "4      天星教育  数据分析师(J10073)   7000.0   经验不限   本科   \n",
       "\n",
       "                                                  描述   工作内容       关键词1  \\\n",
       "0         五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶   在线教育       数据处理   \n",
       "1  生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...   智能硬件  数学/统计相关专业   \n",
       "2                             员工旅游，包住，免费工装，团建聚餐，五险一金   人工智能  数学/统计相关专业   \n",
       "3  带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...    大数据   机器学习建模经验   \n",
       "4  零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...  新闻/出版     Python   \n",
       "\n",
       "        关键词2       关键词3       关键词4    关键词5  \\\n",
       "0        数据库       数据挖掘        NaN     NaN   \n",
       "1  数据分析/挖掘经验    智能硬件学习机         教育    全职工作   \n",
       "2    计算机相关专业  数据分析/挖掘经验       教育相关     NaN   \n",
       "3          R        SAS  数据分析/挖掘经验  Python   \n",
       "4       SPSS        NaN        NaN     NaN   \n",
       "\n",
       "                                                  要求   薪水最小值    薪水最大值  \\\n",
       "0  岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...  8000.0  12000.0   \n",
       "1  1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...  3000.0   8000.0   \n",
       "2  岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...  3000.0   8000.0   \n",
       "3  我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...  7000.0  10000.0   \n",
       "4  工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...  6000.0   8000.0   \n",
       "\n",
       "        公司规模  城市   地区  \n",
       "0    100-499  北京  海淀区  \n",
       "1       0-20  安阳  文峰区  \n",
       "2       0-20  随州  曾都区  \n",
       "3    100-499  成都  双流区  \n",
       "4  1000-9999  郑州   郑州  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.copy()\n",
    "# 定义一个函数来提取城市名称\n",
    "def extract_city(address):\n",
    "    if '·' in address:\n",
    "        return address.split('·')[0]\n",
    "    else:\n",
    "        return address\n",
    "\n",
    "# 定义一个函数来提取地区名称\n",
    "def extract_district(address):\n",
    "    if '·' in address and len(address.split('·')) > 1:\n",
    "        return address.split('·')[1].split('·')[0]\n",
    "    else:\n",
    "        return address\n",
    "\n",
    "# 应用函数到“地址”列，创建新的列“城市”和“地区”\n",
    "df2['城市'] = df2['地址'].apply(extract_city)\n",
    "df2['地区'] = df2['地址'].apply(extract_district)\n",
    "\n",
    "# 删除原始的“地址”列，如果不再需要\n",
    "df2 = df2.drop(columns=['地址'])\n",
    "\n",
    "# 显示修改后的DataFrame的前几行以检查结果\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c51dcfc",
   "metadata": {},
   "source": [
    "## 分词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "252d387f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4560 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     要求\n",
       "0     岗位职责：\\n1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）；\\n2、根据业...\n",
       "1     1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...\n",
       "2     岗位职责：\\n1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数...\n",
       "3     我们需要你：\\n•深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案；\\n•整...\n",
       "4     工作职责:\\n1.负责省区当年，当期销售数据的统计、分析工作。\\n2.负责省区代理商的评估和...\n",
       "...                                                 ...\n",
       "7648  岗位职责：\\n负责山东省电力交易模型编制、编制年度、月1、度、现货等交易策略分析报告并执行电...\n",
       "7652  职位要求：\\n1、 具备良好的心理素质，处事严谨，细心。\\n2、 熟练运用Excel、或者R...\n",
       "7661  1.能较好的理解需求并完成日常工作任务；\\n2.有耐心，具备较强的沟通协调能力；\\n3.要求...\n",
       "7663  职位描述\\n1、完成具体指定的数据统计分析工作:\\n2、对数据指标进行解读，深入分析，并负责...\n",
       "7664  岗位职责：\\n1）负责公司运营指标体系搭建和完善，通过对业务的敏锐洞察和理解，构建数据分析指...\n",
       "\n",
       "[4560 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page=df['要求']\n",
    "pd.DataFrame(page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9ae7f4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['的, 是, 在, 有, 和, 就, 不, 人, 都, 一, 一个, 上, 也, 很, 到, 说, 要, 以, 我们, 会, 走, 好, 国, 去, 来, 用, 你, 们, 着, 工作, 之间, 地, 但是, 由于，一个, 一些, 一, 一起, 第一, 第二, 第三, 第四, 第五, 第六, 第七, 第八, 第九, 第十，他, 她, 它, 这, 那, 这样, 那样, 这些, 那些，因为, 所以, 而且, 虽然, 但是, 如果, 只要, 与, 和, 及, 以及, 并且，了, 过, 在, 有, 是, 我, 我们, 你, 你们, 他, 她, 它, 他们, 她们, 它们, 我, 你, 他, 我们, 你们, 他们, 她们, 它们']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('停用词.txt', 'r', encoding='utf-8') as f:\n",
    "    stopwords = f.readlines()\n",
    "    \n",
    "stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9544e26a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\chenq\\AppData\\Local\\Temp\\jieba.cache\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               ProcessedRequirements\n",
      "0  岗位职责： 1、根据业务需求，完成相关数据挖掘，数据分析工作（占比40%）； 2、根据业务需...\n",
      "1  1．经过公司培训，通过学生在系统中学习产生的数据报告为学生的下一步学习做好规划，制定学习方向...\n",
      "2  岗位职责： 1、深入了解智能学习系统，在后台监测学生学习数据，对学生的日报告、周数据、月数据...\n",
      "3  我们需要你： •深入了解高等教育领域的客户需求与挑战，明确业务场景，设计分析方案； •整合与...\n",
      "4  工作职责: 1.负责省区当年，当期销售数据的统计、分析工作。 2.负责省区代理商的评估和销货...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading model cost 0.898 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
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       "      <td>岗位职责 ：   1 、 根据 业务 需求 ， 完成 相关 数据挖掘 ， 数据分析 工作 （...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 ． 经过 公司 培训 ， 通过 学生 在 系统 中 学习 产生 的 数据 报告 为 学生...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>岗位职责 ：   1 、 深入 了解 智能 学习 系统 ， 在 后台 监测 学生 学习 数据...</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>工作 职责 :   1 . 负责 省区 当年 ， 当期 销售 数据 的 统计 、 分析 工作...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>岗位职责 ：   负责 山东省 电力 交易 模型 编制 、 编制 年度 、 月 1 、 度 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>职位 要求 ：   1 、   具备 良好 的 心理素质 ， 处事 严谨 ， 细心 。   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4557</th>\n",
       "      <td>1 . 能 较 好 的 理解 需求 并 完成 日常 工作 任务 ；   2 . 有 耐心 ，...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4558</th>\n",
       "      <td>职位 描述   1 、 完成 具体 指定 的 数据 统计分析 工作 :   2 、 对 数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4559</th>\n",
       "      <td>岗位职责 ：   1 ） 负责 公司 运营 指标体系 搭建 和 完善 ， 通过 对 业务 的...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4560 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  ProcessedRequirements\n",
       "0     岗位职责 ：   1 、 根据 业务 需求 ， 完成 相关 数据挖掘 ， 数据分析 工作 （...\n",
       "1     1 ． 经过 公司 培训 ， 通过 学生 在 系统 中 学习 产生 的 数据 报告 为 学生...\n",
       "2     岗位职责 ：   1 、 深入 了解 智能 学习 系统 ， 在 后台 监测 学生 学习 数据...\n",
       "3     我们 需要 你 ：   • 深入 了解 高等教育 领域 的 客户 需求 与 挑战 ， 明确 ...\n",
       "4     工作 职责 :   1 . 负责 省区 当年 ， 当期 销售 数据 的 统计 、 分析 工作...\n",
       "...                                                 ...\n",
       "4555  岗位职责 ：   负责 山东省 电力 交易 模型 编制 、 编制 年度 、 月 1 、 度 ...\n",
       "4556  职位 要求 ：   1 、   具备 良好 的 心理素质 ， 处事 严谨 ， 细心 。   ...\n",
       "4557  1 . 能 较 好 的 理解 需求 并 完成 日常 工作 任务 ；   2 . 有 耐心 ，...\n",
       "4558  职位 描述   1 、 完成 具体 指定 的 数据 统计分析 工作 :   2 、 对 数据...\n",
       "4559  岗位职责 ：   1 ） 负责 公司 运营 指标体系 搭建 和 完善 ， 通过 对 业务 的...\n",
       "\n",
       "[4560 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import jieba\n",
    "\n",
    "# 假设stopwords已经被定义\n",
    "\n",
    "# 假设page是您要处理的原始数据\n",
    "page = df['要求']  # 这里应该是您的原始数据\n",
    "\n",
    "# 处理“要求”列中的每一项\n",
    "processed_page = []\n",
    "for req in page:\n",
    "    # 确保req是字符串类型\n",
    "    req_str = str(req)\n",
    "    \n",
    "    # 分割字符串\n",
    "    words = [line.strip('\\n') for line in req_str.split('\\n') if line.strip('\\n') != '']  # 去除空行\n",
    "    \n",
    "    # 去除停用词\n",
    "    processed_words = [word for word in words if word not in stopwords]  # 去除停用词\n",
    "    if processed_words:  # 确保列表不为空\n",
    "        processed_page.append(' '.join(processed_words))  # 将单词列表转换为字符串\n",
    "    else:\n",
    "        processed_page.append('')  # 如果列表为空，添加空字符串\n",
    "\n",
    "# 将处理后的结果转换为DataFrame\n",
    "processed_df = pd.DataFrame(processed_page, columns=['ProcessedRequirements'])\n",
    "\n",
    "# 显示处理后的DataFrame\n",
    "print(processed_df.head())  # 打印前几行数据以检查\n",
    "\n",
    "# 使用结巴分词进行分词\n",
    "processed_df['ProcessedRequirements'] = processed_df['ProcessedRequirements'].apply(lambda x: ' '.join(jieba.lcut(x)) if x else '')\n",
    "\n",
    "# 再次显示处理后的DataFrame\n",
    "processed_df  # 打印前几行数据以检查分词结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "138c84d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "498afe42",
   "metadata": {},
   "outputs": [],
   "source": [
    "word=jieba.lcut(str(processed_page))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ff78245",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>城市</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>安阳</td>\n",
       "      <td>文峰区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>随州</td>\n",
       "      <td>曾都区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>成都</td>\n",
       "      <td>双流区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1000-9999</td>\n",
       "      <td>郑州</td>\n",
       "      <td>郑州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>杭州超级伙伴科技</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>电商/零售行业数据相关经验</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>NaN</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>绍兴</td>\n",
       "      <td>柯桥区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>吾执投资</td>\n",
       "      <td>量化数据研究员</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，零食下午茶，带薪年假，包吃</td>\n",
       "      <td>基金</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>上海</td>\n",
       "      <td>浦东新区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>大数据</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>SQL</td>\n",
       "      <td>能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>昆明</td>\n",
       "      <td>五华区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td>NaN</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>20-99</td>\n",
       "      <td>西安</td>\n",
       "      <td>雁塔区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>20-99</td>\n",
       "      <td>徐州</td>\n",
       "      <td>云龙区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4560 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              标题             名称       薪水   经历要求 学历要求  \\\n",
       "0           东方优特          数据分析师  10000.0   经验不限   本科   \n",
       "1       松鼠Ai智能学习          数据分析师   5500.0   经验不限   本科   \n",
       "2          羽山机器人          数据分析师   5500.0   经验不限   本科   \n",
       "3        麦可思数据科技      数据分析师（校招）   8500.0  在校/应届   本科   \n",
       "4           天星教育  数据分析师(J10073)   7000.0   经验不限   本科   \n",
       "...          ...            ...      ...    ...  ...   \n",
       "7648    杭州超级伙伴科技          数据分析师   7000.0   经验不限   本科   \n",
       "7652        吾执投资        量化数据研究员  10500.0   经验不限   本科   \n",
       "7661          洲宇         数据分析专员   5000.0   经验不限   本科   \n",
       "7663  西安易升电子有限公司         证券数据分析   7500.0   经验不限   本科   \n",
       "7664       江苏蕲臻堂        电商数据分析师   6000.0   经验不限   本科   \n",
       "\n",
       "                                                     描述         工作内容  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶         在线教育   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...         智能硬件   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金         人工智能   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...          大数据   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...        新闻/出版   \n",
       "...                                                 ...          ...   \n",
       "7648  五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖         电子商务   \n",
       "7652                                 五险一金，零食下午茶，带薪年假，包吃           基金   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假          大数据   \n",
       "7663                                                NaN  电子/半导体/集成电路   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游        批发/零售   \n",
       "\n",
       "            关键词1           关键词2       关键词3       关键词4        关键词5  \\\n",
       "0           数据处理            数据库       数据挖掘        NaN         NaN   \n",
       "1      数学/统计相关专业      数据分析/挖掘经验    智能硬件学习机         教育        全职工作   \n",
       "2      数学/统计相关专业        计算机相关专业  数据分析/挖掘经验       教育相关         NaN   \n",
       "3       机器学习建模经验              R        SAS  数据分析/挖掘经验      Python   \n",
       "4         Python           SPSS        NaN        NaN         NaN   \n",
       "...          ...            ...        ...        ...         ...   \n",
       "7648   数学/统计相关专业  电商/零售行业数据相关经验  数据分析/挖掘经验        NaN   数学/统计相关专业   \n",
       "7652      Python            NaN        NaN        NaN      Python   \n",
       "7661         SQL         Python          R  数学/统计相关专业         SQL   \n",
       "7663  金融行业数据相关经验         风控相关经验   证券数据归纳分析        NaN  金融行业数据相关经验   \n",
       "7664      Python            NaN        NaN        NaN      Python   \n",
       "\n",
       "                                                     要求   薪水最小值    薪水最大值  \\\n",
       "0     业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...  8000.0  12000.0   \n",
       "1     经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...  3000.0   8000.0   \n",
       "2     深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...  3000.0   8000.0   \n",
       "3     需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...  7000.0  10000.0   \n",
       "4     负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...  6000.0   8000.0   \n",
       "...                                                 ...     ...      ...   \n",
       "7648  负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...  6000.0   8000.0   \n",
       "7652  职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...  8000.0  13000.0   \n",
       "7661  能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...  4000.0   6000.0   \n",
       "7663  职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...  5000.0  10000.0   \n",
       "7664  负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...  5000.0   7000.0   \n",
       "\n",
       "           公司规模  城市    地区  \n",
       "0       100-499  北京   海淀区  \n",
       "1          0-20  安阳   文峰区  \n",
       "2          0-20  随州   曾都区  \n",
       "3       100-499  成都   双流区  \n",
       "4     1000-9999  郑州    郑州  \n",
       "...         ...  ..   ...  \n",
       "7648    100-499  绍兴   柯桥区  \n",
       "7652       0-20  上海  浦东新区  \n",
       "7661       0-20  昆明   五华区  \n",
       "7663      20-99  西安   雁塔区  \n",
       "7664      20-99  徐州   云龙区  \n",
       "\n",
       "[4560 rows x 18 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 停用词列表，去除换行符\n",
    "stopwords = set([\n",
    "    '的', '是', '在', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '以',\n",
    "    '我们', '会', '走', '好', '国', '去', '来', '用', '你', '们', '着', '工作', '之间', '地', '但是', '由于', '一个',\n",
    "    '一些', '一', '一起', '第一', '第二', '第三', '第四', '第五', '第六', '第七', '第八', '第九', '第十', '他', '她', '它',\n",
    "    '这', '那', '这样', '那样', '这些', '那些', '因为', '所以', '而且', '虽然', '但是', '如果', '只要', '与', '和', '及',\n",
    "    '以及', '并且', '了', '过', '在', '有', '是', '我', '我们', '你', '你们', '他', '她', '它', '他们', '她们', '它们',\n",
    "    '我', '你', '他', '我们', '你们', '他们', '她们', '它们', '1', '、', '职责', '要求', '岗位', '为', '2', '3', '，',\n",
    "    '。', '·', '（', '）', '；', '和', '对', '/n', '岗位职责', ',', '.', ':', ';', '：', '\"\"', '根据', '[', \"'\",\n",
    "    '等', ']', '\\\\', '4', '5', '6', '7', '8', '并', '•', '【', '】', '﹣', '！', ')', '*', '—', '“', '/',\n",
    "    '-', 't', '+', '\\n', '．', '《', '》'\n",
    "])\n",
    "\n",
    "# 假设page是您要处理的原始数据\n",
    "page = df2['要求']  # 这里应该是您的原始数据\n",
    "\n",
    "# 处理“要求”列中的每一项\n",
    "processed_page = []\n",
    "for req in page:\n",
    "    # 确保req是字符串类型\n",
    "    req_str = str(req)\n",
    "\n",
    "    # 使用结巴分词进行分词\n",
    "    words = jieba.lcut(req_str)\n",
    "\n",
    "    # 去除停用词\n",
    "    processed_words = [word for word in words if word not in stopwords and word!= ' ']  # 去除停用词和空格\n",
    "    if processed_words:  # 确保列表不为空\n",
    "        processed_page.append(' '.join(processed_words))  # 将单词列表转换为字符串\n",
    "    else:\n",
    "        processed_page.append('')  # 如果列表为空，添加空字符串\n",
    "\n",
    "# 直接用处理后的结果替换原来的‘要求’列\n",
    "df2['要求'] = processed_page\n",
    "\n",
    "# 显示处理后的原始数据框（包含替换后的列）\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "959b07f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'业务': 2250, '需求': 2068, '完成': 2033, '相关': 4631, '数据挖掘': 235, '数据分析': 1957, '占': 27, '比': 37, '40%': 13, '撰写': 698, '报告': 1142, '支持': 590, '各': 452, '部门': 700, '临时': 52, '数据': 6389, '20%': 18, '任职': 1933, 'xa0': 1048, '教育': 557, '类': 640, '经验': 3452, '者': 1564, '优先': 3705, '课题': 48, '研究': 983, '精通': 366, '至少': 294, '一种': 269, '挖掘': 555, '软件': 1372, '操作': 515, '如': 510, 'SAS': 102, 'SPSS': 123, 'R': 184, '熟悉': 2543, '应用': 760, '常用': 261, '统计分析': 218, '方法': 326, '统计': 663, '检验': 63, '敏感': 199, '具有': 2001, '优秀': 661, '数据处理': 472, '能力': 6220, '学习': 2145, '熟练': 1311, '使用': 1541, 'EXCEL': 529, 'PPT': 253, '办公': 729, '做事': 120, '认真': 236, '严谨': 134, '具备': 2246, '执行': 478, '较强': 1034, '责任心': 871, '良好': 2283, '逻辑': 497, '分析': 3868, '善于': 441, '思考': 98, '9': 374, '沟通': 2786, '团队': 2247, '合作': 1085, '意识': 513, '经过': 21, '公司': 3229, '培训': 1328, '通过': 599, '学生': 537, '系统': 1210, '中': 865, '产生': 44, '下': 215, '一步': 9, '做好': 386, '规划': 545, '制定': 687, '方向': 407, '及时': 653, '跟': 61, '家长': 231, '情况': 517, '让': 191, '可以': 381, '了解': 1088, '进展': 61, '深入': 252, '智能': 118, '后台': 63, '监测': 174, '日': 39, '周': 61, '月': 500, '进行': 2843, '正确': 29, '解读': 70, '建立': 375, '学习机': 13, '用户': 780, '档案': 66, '定期': 548, '更新': 207, '录入': 72, '重要': 90, '信息': 974, '跟进': 415, '进度': 219, '状态': 66, '将': 378, '准确': 197, '呈现': 56, '给': 194, '过程': 391, '出现': 72, '各种': 400, '问题': 1242, '疏导': 16, '解决': 589, '心理': 57, '建设': 369, '参加': 189, '组织': 596, '配合': 369, '市场': 1028, '必要': 51, '宣传': 98, '活动': 553, '年龄': 133, '25': 44, '岁': 188, '38': 10, '本科': 1775, '以上学历': 1898, '能够': 1446, '操作电脑': 5, 'OFFICE': 253, '爱': 47, '强烈': 208, '提升': 548, '自我': 141, '性格外向': 30, '结果': 305, '外化': 1, '喜欢': 188, '孩子': 188, '热爱': 402, '行业': 1740, '对于': 103, '人工智能': 180, '价值': 256, '意义': 8, '深刻': 21, '理解': 399, '较': 973, '强': 1964, '理解能力': 89, '能': 1490, '发现': 229, '协作': 497, '同事': 101, '高效': 248, '需要': 489, '高等教育': 7, '领域': 506, '客户': 3230, '挑战': 156, '明确': 58, '场景': 156, '设计': 1197, '方案': 769, '整合': 177, '清洗': 265, '多源': 4, '包括': 722, '成果': 103, '课程': 486, '评价': 48, '教学': 451, '反馈': 425, '运用': 280, '或': 1981, '机器': 227, '建模': 304, '识别': 97, '关键': 82, '趋势': 108, '诊断': 61, '定位': 142, '基于': 247, '提供': 1720, '策略': 389, '建议': 426, '复盘': 50, '项目': 2828, '输出': 269, '标准': 323, '成员': 121, '紧密': 65, '确保': 597, '质量': 508, '参与': 1056, '产品': 3230, '改进': 255, '关注': 176, '迭代': 140, '分析方法': 66, '体验': 181, '持续': 326, '最新': 80, '前沿性': 2, '统计学': 455, '数学': 613, '科学': 152, '经济学': 118, '专业': 3075, '可视化': 253, 'PYTHON': 712, '工具': 835, '驱动': 136, '思维': 313, '决策': 218, '转化': 109, '敏感性': 61, '注重': 65, '细节': 69, '解决问题': 289, '新': 714, '技术': 1813, '加': 212, '分项': 148, '背景': 377, '机构': 155, '政府部门': 17, '合作项目': 8, '您': 317, '充满': 59, '机会': 383, '环境': 473, '国内外': 117, '顶尖': 15, '大学': 86, '专家': 133, '交流': 239, '成长': 241, '项目经理': 203, '带领': 69, '交付': 295, '申请': 134, '方式': 155, '有意者': 9, '请': 106, '简历': 126, '发送至': 8, '标题': 157, '注明': 8, '应聘': 39, '分析师': 191, '”': 314, '负责': 4162, '省区': 8, '当年': 5, '当期': 2, '销售': 919, '代理商': 25, '评估': 325, '销货': 2, '指导': 370, '客情': 13, '维护': 1359, '资格': 447, '亲和力': 141, '表达能力': 401, '积极进取': 21, '财务会计': 9, '市场营销': 136, '工商管理': 48, '福利待遇': 242, '六险': 33, '一金': 488, '各项': 237, '过节': 9, '福利': 913, '年度': 226, '健康': 122, '体检': 173, '季度': 71, '奖金': 237, '年终奖金': 41, '五天': 31, '工作制': 54, '享受': 190, '国家': 281, '法定': 301, '节假日': 405, '带薪': 403, '假期': 126, '年': 1073, '假': 377, '婚假': 79, '病假': 35, '产假': 93, '各个': 54, '内部': 301, '外派': 17, '每年': 165, '员工': 625, '订阅': 3, '上万': 3, '本': 175, '各类': 443, '期刊杂志': 2, '图书': 18, '再': 35, '补贴': 308, '旅游': 216, '聚餐': 86, '综艺': 2, '算法': 455, '讲师': 32, 'UFEFF': 31, '独立': 684, '研发': 475, '领导': 385, '交代': 83, '其他': 941, '事宜': 73, '始终保持': 4, '高度': 130, '知识': 609, '案例': 106, '统招': 223, '应届生': 210, '可': 849, '培养': 199, '掌握': 658, '数理统计': 24, 'BI': 145, 'MYSQL': 281, '条理清楚': 5, '事业': 135, '从事': 230, '互联网': 335, '语言': 439, '积极': 322, '性格开朗': 101, '能说会道': 2, '精神': 762, '自律': 8, '谋': 2, '长远': 5, '发展': 831, '接受': 495, '兼职': 34, '待遇': 304, '7K': 10, '~': 138, '11K': 4, '(': 445, '具体': 166, '面谈': 37, '整理': 692, '理论': 116, '深度': 288, '开发': 1589, '及其': 117, '扎实': 195, '基础': 602, '其一': 5, '即可': 50, '敏感度': 132, '文档': 533, '写作能力': 55, '勇于': 28, '金融': 565, '从业': 163, 'CDA': 10, '培训者': 3, '数据库': 1103, '基本知识': 22, 'SQL': 633, '语句': 86, '自主': 110, '增删': 13, '改查': 9, '尤其': 41, 'WORD': 173, '电信': 22, '逻辑思维': 468, '利用': 203, '大': 1002, '计算机专业': 47, '全球': 98, '财务': 281, '债券': 24, '采集': 308, '审核': 206, '准确性': 142, '专业性': 15, '高质量': 95, '经济': 282, '信息管理': 82, '计算机': 841, '技能': 388, '五险': 570, '600': 7, '元': 202, '租房': 25, '研究生': 86, '800': 6, '中午饭': 2, '补': 18, '年终奖': 134, '绩效': 241, '奖励': 101, '双休': 368, '休息': 160, '主要': 302, '搜集整理': 14, '得到': 58, '什么': 41, '？': 83, '完善': 364, '职业培训': 12, '体系': 441, '一对一': 41, '导师': 43, '长': 25, '达': 47, '一年': 165, '半': 16, '新人': 70, '计划': 540, '灵活': 79, '换岗': 5, '轮岗': 37, '制度': 139, '多元化': 22, '全方位': 41, '职业': 255, '尽展': 5, '才华': 14, '核心技术': 12, '完全': 18, '成为': 147, '大牛': 27, '极具': 11, '竞争力': 128, '薪资': 596, '分红': 51, '股权': 42, '中长期': 30, '激励': 81, '尽享': 5, '成功': 91, '荣耀': 5, '商业保险': 25, '落户': 10, '筑巢': 5, '全面': 143, '关怀': 27, '无': 200, '后顾之忧': 8, '扁平化': 28, '架构': 244, '轻松': 50, '简单': 144, '办公室': 51, '文化': 144, '专注': 71, '加入': 258, '万得': 17, '投资': 205, '跨界': 11, '人才': 178, '做': 277, 'FINTECH': 6, '先锋': 7, '时间': 520, '三个': 22, '按天': 2, '结算': 58, '固定': 50, '收益': 22, '交易': 202, '助理': 79, '证券': 119, '公司总部': 12, '正式': 60, '编制': 249, '资格证': 65, '二十六岁': 2, '以下': 227, '二本': 10, '一本': 29, '职位': 666, '描述': 305, '从': 266, '不同': 215, '数据源': 53, '提取': 89, '深入分析': 28, '模式': 106, '构建': 145, '清晰': 231, '帮助': 281, '背后': 50, '故事': 11, '提供数据': 127, '见解': 45, '跟踪': 452, '效果': 192, '不断': 251, '优化': 1109, '流程': 896, '核心': 224, '交办': 206, '工科': 30, '限': 280, '浓厚兴趣': 34, '以上': 1158, '编程语言': 177, '快速': 455, '强大': 30, '出色': 82, '有效': 366, '精细化': 22, '分层': 19, '配置': 222, '营销': 284, '达成': 125, '目标': 454, '洞察': 187, '给出': 80, '分': 85, '群': 18, '按': 161, '运营': 897, '侧': 18, '加以分析': 3, '差异': 16, '异常': 143, '波动': 22, '根因': 4, '日常': 726, '监控': 429, '制作': 311, '解决方案': 421, '画像': 13, '特征': 38, '圈选': 5, '形成': 144, '清单': 22, '结合': 188, '销售策略': 46, '必会': 2, '取': 4, '符合': 88, '录用': 88, '编写': 633, '经营': 206, '两年': 45, '计算机相关': 242, '最佳': 21, '通信': 111, '运营商': 28, '考虑': 569, 'CPDA': 5, '认证': 134, '看': 58, '统计数据': 12, '毕业': 181, '2023': 25, '招聘': 356, '截止': 15, '2024.12': 4, '31': 13, '比例': 19, '分享': 107, '详情': 29, '2024': 39, '日期': 6, '情报': 22, '自己': 216, '股票交易': 3, '协助': 1096, '买方': 3, '股票市场': 5, '股票': 39, '求职者': 32, '应': 53, '如下': 16, '英文': 130, '口语': 57, '自由': 41, '粤语': 8, '英语': 433, '书面': 62, '注': 19, '低者': 2, '直接': 54, '发送': 29, '编辑': 117, '英文简历': 4, '宗旨': 8, '智慧': 116, '成就': 23, '望': 5, '共事': 13, '真正': 14, '创造': 66, '人生价值': 2, '原理': 174, '调研': 480, '针对': 219, '问卷': 49, '模型': 469, '测试': 471, '心理学': 59, '社会学': 43, '硕士': 139, '定性': 33, '定量': 30, '处理': 914, '熟练掌握': 507, '收视率': 1, '踏实': 208, '细心': 174, '数理逻辑': 2, '一定': 868, '文字': 269, '接口': 41, '获取': 80, '开展': 326, '知识产权': 86, '专利': 235, '论文': 34, '条件': 155, '之一': 46, '实践经验': 51, '大规模': 33, '集': 32, '编程': 491, 'SPARK': 144, 'KAFKA': 57, 'HBASE': 56, 'MONGODB': 38, '图像识别': 9, '图谱': 21, '知识者': 11, '区块': 24, '链': 28, '交易所': 3, '钱包': 2, '函数': 73, 'XMIND': 11, '导图': 8, '有过': 66, '行为': 102, '消费者': 69, '数据模型': 116, '预测': 153, '牵头': 4, '对接': 292, '合作伙伴': 56, '支撑': 126, '电力': 182, '改革': 13, '政策': 241, '文件': 216, '配套': 24, '管理': 2103, '办法': 18, '山东电力': 7, '现行': 7, '规则': 100, '现货交易': 12, '广东': 10, '浙江': 43, '省域': 4, '交易规则': 19, '负荷': 20, '每日': 69, '实际': 191, '用电量': 8, '天气': 13, '变化': 105, '对全': 6, '运行': 243, '月度': 104, '现货': 30, '批发市场': 4, '零售': 84, '气': 12, 'MATLAB': 51, '数学分析': 5, '一门': 80, '认真负责': 105, '性格': 64, '稳定': 174, '且': 139, '周一': 81, '至': 132, '周五': 71, '早上': 19, '00': 533, '晚上': 13, '18': 186, '中午': 30, '小时': 127, '休息时间': 12, '地点': 296, '济南市': 5, '历城区': 4, '汉峪': 4, '金谷': 4, 'A3': 8, '栋': 26, '601': 4, '亮点': 34, '同行': 28, '更': 182, '高': 303, '提成': 268, '甲级': 11, '写字楼': 26, '试用期': 114, '转正': 153, '缴纳': 137, '签订': 68, '劳务': 8, '合同': 123, '带薪休假': 42, '丰富': 203, '团建': 197, '拼': 9, '才会赢': 2, '安泰': 2, '售电': 13, '期待': 75, '求职意向': 4, '先发': 2, '不予考虑': 2, '每天': 55, '居家': 10, '在线': 62, '简报': 4, '推送': 22, '内容': 719, '空气质量': 9, '专项': 92, '大气': 67, '污染源': 23, '现场': 279, '巡查': 15, '各级': 16, '政府': 143, '治污': 2, '降霾': 2, '指定': 35, '群体': 22, '35': 66, '周岁': 52, '大学本科': 112, '气象学': 2, '大气物理': 4, '化学': 76, '生态学': 2, '环境工程': 26, '土壤学': 1, '水文': 3, '水资源': 3, '学等': 5, '理工科': 141, '咨询': 765, '一市': 1, '一策': 2, '一区': 1, '协调': 762, '综合': 229, '中级': 55, '技术职称': 1, '取得': 22, '注册': 48, '资格证书': 45, '整体素质': 1, '特别': 64, '上述': 12, '适当': 50, '放宽': 87, '敏感数据': 3, '搜集': 90, '标注': 157, '排重': 3, '二次': 11, '加工': 101, '认真细致': 33, '服从': 78, '单位': 84, '纪律': 17, '安排': 508, '平台': 1135, '新闻': 66, '事件': 71, '媒体': 193, '竞争': 53, '走势': 24, '关系': 299, '舆论': 16, '实时': 94, '他人': 25, '提出': 355, '保持': 171, '热情': 144, '客观': 20, '接纳': 6, '态度': 104, 'JOB': 6, 'SUMMARY': 2, 'RESPONSIBLE': 10, 'FOR': 26, 'EXECUTING': 1, 'TECHNICAL': 6, 'AND': 155, 'STATISTICAL': 2, 'ASPECTS': 1, 'OF': 57, 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'BS': 3, 'FRESH': 2, 'GRADUATES': 3, 'STATISTICS': 4, 'RELATED': 4, 'BACKGROUND': 4, 'LESS': 2, 'THAN': 4, 'YEAR': 2, 'EXPERIENCES': 1, 'FAMILIAR': 2, '&': 91, 'BASIC': 3, 'CODING': 3, 'KNOWLEDGE': 9, 'EXPERIENCE': 13, 'IS': 20, 'NICE': 13, 'HAVE': 5, 'GOOD': 9, 'SKILLS': 21, 'PROBLEM': 4, 'SOLVING': 5, 'EFFECTIVE': 1, 'COMMUNICATION': 6, 'MANAGEMENT': 18, 'SKILL': 3, 'NEEDED': 4, 'VBA': 20, 'VISUALIZATION': 1, 'TOOL': 1, 'E': 27, 'G': 11, 'TABLEAU': 65, 'WOULD': 2, 'A': 68, 'PLUS': 5, 'FMCG': 1, 'SERVICE': 10, 'RETAIL': 1, 'INDUSTRY': 4, '电商': 270, '购物': 14, '当前': 18, '满意度': 138, '方面': 244, '文献检索': 9, '平时': 16, '调查': 135, '协商': 10, '随时': 29, '联系': 115, '落实': 84, '数据报表': 58, '模板': 19, '常态': 14, '推动': 293, '业务部门': 125, '广告': 152, '抓取': 51, '亚马逊': 58, '页面': 27, '爬虫': 102, '师': 42, '计算机信息': 6, '工程': 314, '结论': 40, '浓烈': 4, '兴趣': 174, '钻研': 78, '创新': 293, '大量': 34, '频次': 5, '成': 33, '向': 168, '汇报': 175, '服务': 976, '收集': 619, '痛点': 53, '优质': 72, '推广': 358, '试点': 8, '引进': 11, '功能': 233, '点': 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57, '格式': 45, '甄别': 21, '真伪': 22, '间': 51, '潜在': 133, '工艺': 77, '及时性': 35, '准确度': 18, '立项': 49, '落地': 214, '上线': 177, '拓展': 205, '沉稳': 22, '充满热情': 49, '小': 120, '语种': 49, '金融市场': 25, '敏锐': 119, '洞察力': 84, '例如': 59, '证等': 16, '数据服务': 43, '四级': 108, '留学': 231, '饭补': 14, '高温': 35, '父亲节': 5, '母亲节': 5, '礼品': 86, '背书': 1, '资料': 304, '交割': 3, '校验': 19, '代码': 117, '前期': 69, '资产': 115, '软件工程': 97, '同': 20, '及时发现': 70, '引导': 97, '环节': 67, '节点': 22, '商业化': 17, '异常情况': 21, '反向': 5, '业务流程': 118, '调优': 101, '产品设计': 93, '沉淀': 33, '框架': 269, '提炼': 81, '达到': 70, '所': 257, '需': 135, '资源': 272, '衔接': 15, '方及': 3, 'B': 61, 'C': 132, '跨部门': 85, 'POWERBI': 26, '帆软': 29, 'SUPERSET': 4, 'METABASE': 2, '主流': 236, '数仓': 18, '公安': 14, '执法': 4, '案件': 36, '涉及': 92, '调查取证': 4, '希望': 121, '计算机网络': 31, '基础知识': 97, '主动性': 64, '自我管理': 11, '网络安全': 97, '电子': 171, '取证': 16, '舆情': 102, '预警': 63, '研判': 25, '遇': 4, '突发': 20, '时': 91, '公关': 16, '更佳': 45, '社会': 66, '热点': 61, '传播': 46, '处置': 13, '食品行业': 2, '发展趋势': 50, '有所': 23, '会说会写': 2, '满足': 129, '审美': 17, '突出': 33, '懂': 62, '社交': 91, '现状': 58, '知道': 14, '如何': 31, '把': 135, '网络': 570, '作为': 116, '搜索': 54, '软件开发': 69, '面向对象': 5, '思想': 19, 'PANDAS': 26, 'NUMPY': 17, 'PLOTLY': 1, 'SKLEARN': 5, '聚类': 19, '能用': 1, '查询': 77, 'JAVA': 229, 'SCRIPT': 2, 'CSS': 23, 'HTML': 22, '前端开发': 12, 'FLASK': 9, 'DJANGO': 10, '前后': 18, '端的': 15, 'GIT': 16, 'GITHUB': 8, 'GITLAB': 10, '编码': 54, '习惯': 82, '听说读写': 49, '微软': 5, '思路清晰': 57, '承担': 145, '乐于': 95, '上报': 41, '科技': 180, '部分': 100, '管理系统': 41, '编写程序': 3, '简明': 6, '二': 207, '软件包': 1, '最好': 42, '气象': 42, '角度': 29, '合理': 154, '甲方': 29, '扩散': 5, '污染': 53, '机理': 3, '防治': 13, '控制措施': 2, '专业知识': 109, '空气': 3, '环境监测': 23, '应急': 51, '风险': 176, '大气环境': 4, '状况': 43, '周期性': 17, '管理层': 48, '管理工作': 135, '给与': 12, '归纳': 95, '自研': 15, '三方': 17, '交叉': 18, '验证': 95, '埋点': 7, '治理': 177, '建设性': 8, '带动': 6, '文化氛围': 5, '信息技术': 68, '通信工程': 22, '突出者': 13, '学历': 288, 'POSTGRESQL': 35, 'ORACLE': 244, '型': 103, '基本': 324, '结构化': 38, '非': 77, 'FINEBI': 16, 'QUICK': 4, '其中': 73, '整个': 47, '完整': 87, '接入': 26, 'HADOOP': 161, 'STORM': 12, 'ELASTICSEARCH': 17, 'GOLANG': 6, 'NODEJS': 2, '网络平台': 16, '反': 20, '爬': 9, '跨境': 93, '中型': 1, '实习生': 95, '归因': 6, '灵活运用': 5, '专题': 51, '有力': 17, '认知': 41, '输入': 10, '匹配': 54, '出众': 9, '乐观': 74, '皮实': 4, '驱动力': 34, '质控': 14, '擅长': 108, '强者': 17, '介绍': 132, '软件技术': 3, '此为': 1, '纯': 8, '技术性': 5, '声明': 1, '无需': 30, '出资': 2, '经济模型': 3, '考核': 122, '后': 345, '上岗': 23, '不肯': 1, '下功夫': 1, '勿扰': 26, '新手': 6, '地方': 24, '多': 203, '20': 103, '30': 439, '理科': 20, '竞赛': 67, '奖项': 9, '守信': 31, '和善': 1, '乐于助人': 2, '安静': 6, '纠正': 15, '遇到': 66, '懂得': 27, '反思': 10, '实践': 83, '自理能力': 1, '心理素质': 26, '探索': 102, '敢于': 27, '面对': 37, '困难': 16, '遇到困难': 1, '轻言': 1, '放弃': 3, '肯': 25, '自律性': 8, '17': 96, '工资待遇': 14, '按照': 203, '无责': 51, '3000': 36, '餐补': 69, '后期': 70, '成绩': 45, '优秀者': 94, '2W': 1, '初创': 9, '小微': 1, '氛围': 169, '适合': 56, '努力': 40, '赚钱': 22, '朋友': 23, '想': 72, '找': 18, '热闹': 1, '这里': 114, '可能': 73, '规模': 38, '不是': 39, '有缘': 2, '志同道合': 22, '伙伴': 39, '开心': 5, '起点': 7, '相遇': 2, '彼此': 3, '理想': 22, '名额有限': 1, '欢迎': 188, '诚心': 1, '小伙伴': 43, '收集整理': 66, '改善': 102, '市场竞争': 5, '程度': 33, '选品': 32, '把握': 56, '同类产品': 13, '全过程': 34, '内外部': 39, '功能设计': 27, '项目管理': 164, '技术开发': 15, '开发进度': 24, '版本': 45, '评审': 61, '发布': 131, '新品': 43, '热卖': 8, '潜力': 38, '采购': 204, '控制': 179, '专科': 67, '水平': 128, '有无': 26, '谢谢': 12, '入职': 228, '专人': 19, '一带': 17, '脚踏实地': 5, '电子商务': 109, '外贸': 223, '激情': 75, '对待': 35, '生活': 74, '物质': 6, '追求': 41, '高薪': 29, '包含': 58, '售后': 131, '利润': 30, '竞争对手': 75, '客服': 38, '生命': 3, '周期': 42, '库存': 150, '指出': 4, '问题所在': 2, '热销': 12, '品': 9, '级': 106, '半年': 25, '国内': 150, '专研': 8, '抗压': 377, 'LISTING': 13, '追踪': 58, '考察': 16, '第三方': 25, '软件工具': 6, '产品销售': 29, 'CPC': 4, '调整': 197, '表现': 113, '阶段': 84, '可行性': 57, '大专': 161, 'SASS': 1, '已有': 27, '隐含': 8, '基金': 119, '经理': 260, '重本': 1, '自动化': 263, '电子信息': 31, '优异': 20, '学习态度': 8, '愿意': 179, '长期': 224, '种': 18, '接触': 61, '见识': 6, '少有': 4, '高性能': 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'宁波': 11, '鄞州区': 3, '有关': 83, '历史数据': 18, '安全': 381, '密切合作': 15, '共同': 101, '探讨': 8, '及大': 13, '图纸': 26, '绘图': 26, '地图': 52, '制图': 44, '监测数据': 20, '核对': 44, '材料': 171, '特殊': 48, '重': 17, '准备': 122, '调度会': 5, 'ORIGIN': 11, '軟件': 6, '対': 3, '迸行': 3, '排序': 5, '对比': 24, '督察': 5, '临时性': 29, '写手': 5, '文字编辑': 10, '公文': 25, '独特': 19, '意见建议': 7, '想法': 56, '北京': 131, '玖': 4, '兴业': 4, '商贸': 5, '有限公司': 87, '一家': 83, '体重': 4, '肠道': 4, '慢性病': 8, '调理': 4, '服务体系': 4, '创业': 65, '生态': 49, '广大': 10, '分销商': 4, '实实在在': 3, '受益': 9, '大家': 33, '一致': 23, '拳头产品': 4, '然皓': 4, '优品': 8, '代餐': 6, '粉': 7, '全国': 122, '各地': 21, '世界各地': 5, '老客户': 65, '数以万计': 4, '远离': 6, '肥胖': 2, '卖萌': 2, '狮益': 2, '生元': 2, '很多': 29, '便秘': 2, '带来': 16, '困扰': 2, '理念': 59, '身体': 10, '一批': 8, '富裕': 2, '起来': 8, '短': 38, '视频': 151, '22': 39, '26': 8, '前': 95, '先': 23, '一下': 12, '方便': 20, '拍摄': 21, '多多': 13, '店': 18, '国氏全': 4, '营养素': 4, '到然': 4, '皓': 4, '淘宝': 8, '抖店': 2, '营养': 9, '排查': 76, '实况': 3, '成效': 11, '有序': 19, '归档': 74, '类型': 57, '投递': 76, '天气形势': 3, '数值': 16, '专业技能': 55, '积极性': 18, '高且': 5, '交通': 109, '等等': 41, '应用程序': 9, 'uf0b7': 36, '对点餐': 1, '实验设计': 3, '因果': 3, '推断': 3, '自然实验': 1, '生命周期': 61, '高级': 79, '985': 110, '知名': 46, 'ETC': 5, '快消': 24, '一周': 36, '上海市': 9, '徐汇': 1, '月光': 1, '200': 27, '邮箱': 29, '投': 33, '依据': 78, '整体': 167, '业绩': 140, '编排': 24, '公司业绩': 11, '最大化': 17, '受众': 9, '人群': 28, '市场需求': 38, '节目': 13, '商品': 110, '部': 28, '频道': 3, '确定': 58, '播出': 6, '统筹安排': 10, '标签': 36, '上手': 6, '活跃': 62, '开放': 41, '特殊要求': 6, '愿': 8, '一同': 11, '房补': 10, '水果': 20, '卡': 26, '公司简介': 28, '维恩': 3, '成立': 65, '阿里': 91, '宝洁': 4, '市面上': 3, '唯一': 16, '全量': 3, '全': 132, '积累': 72, '前沿': 37, '方法论': 40, '扩张': 10, '技术人员': 21, '优厚': 19, '上升': 17, '通道': 66, '早期': 10, '期权': 20, '个人': 212, '研及': 3, '顺利开展': 9, 'FPS': 2, '更优': 7, '人际沟通': 25, 'ESG': 3, '汇聚': 6, '通用': 27, '迅速': 47, 'c++': 15, '构架': 9, '可带': 2, '佳': 35, '滴滴': 10, '国际化': 6, '朝': 4, '正确方向': 1, '充分': 73, '尽快': 13, '>': 100, 'MUST': 4, '语法': 18, '300': 24, '240': 1, '健身房': 17, '下午茶': 80, '享用': 1, '留用': 5, '宏等': 4, '较完整': 4, '数': 5, '未知': 8, '挑战性': 28, '制': 30, '八小时': 16, '公平': 31, '透明': 25, '届': 88, '日志': 35, '查看': 16, '请求': 8, '按质': 14, '制订': 23, '预案': 11, '升级': 115, '失误': 3, '投诉': 55, '‘': 7, '偏向': 6, '数据管理': 112, '有人': 23, '带': 58, '认识': 31, '调试': 214, 'KETTLE': 25, '稳重': 24, '建立健全': 9, '集团': 112, '决策依据': 16, '喜爱': 13, '呈现出': 2, '入库': 48, '回补': 2, '交予': 5, '态势': 15, '数字化': 119, '物': 58, '联网': 58, '品牌': 271, '设置': 47, '专属': 10, '而是': 6, '营销策划': 14, '训练': 78, '高等数学': 3, '未来': 113, '公办': 8, '公众': 48, '四川': 5, '创一': 4, '登记': 44, '填写': 23, '表单': 8, '我司': 35, '度后': 2, '以此': 7, '邀请': 16, '价格指数': 3, '积极参与': 24, '指数': 16, '报及': 2, '品种': 26, '文章': 44, '加工能力': 4, '农业': 41, '贸易': 41, '同学': 32, '不会': 28, '实证': 15, '现在': 18, '多少': 2, '咱们': 4, '就用到': 1, '辅导': 88, '难度': 4, '60': 16, '每个': 49, '大概': 2, '10h': 1, '来看': 2, '完': 5, '之后': 14, '拿到': 11, '不错': 7, '费用': 68, '低': 16, '性价比': 3, '超高': 5, '还': 76, '描述统计': 1, '显著性': 1, '序列': 23, '面板': 5, '中介': 15, '效应': 2, '方程': 2, 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'合规': 23, '因人而异': 2, '大连市': 3, '高新': 8, '园区': 41, '火炬': 6, '壹号': 2, '楼下': 2, '就是': 44, '地铁站': 19, '早九晚': 10, '大小': 40, '即': 51, '劳动合同': 37, '安全感': 2, '统筹': 50, '保险': 82, '缴五险': 4, '年轻': 60, '活力': 31, '种类': 5, '味蕾': 1, '报价': 58, '析': 3, '咨询服务': 100, '年轻人': 18, '不用': 19, '拘束': 1, '年末': 2, '放长假': 1, '定时': 39, '考研': 30, '失利': 1, '行': 12, '最少': 12, '吃苦': 24, '肯干': 23, '拼搏精神': 3, '学术研究': 9, '月入': 5, '过万': 8, '人为': 2, '介入': 5, '成因': 9, '溯源': 12, '智能化': 24, '研发部门': 8, '硬件': 103, '运维': 444, '应用化学': 9, '药学': 37, '色谱': 7, '质谱': 4, '光谱': 4, '分析仪器': 14, '民营': 3, '500': 39, '团餐': 2, 'TOP1': 2, '2008': 2, '奥运会': 2, '2022': 18, '冬奥会': 2, '餐饮': 44, '就业': 14, '民营企业': 5, '排名第': 2, '16': 50, '名': 46, '万': 66, '爆品': 4, '货物': 40, '新融': 11, '简介': 21, '湖南': 20, '大信': 1, '会计师': 14, '事务所': 31, '普通': 13, '合伙': 3, '长沙': 9, '中磊': 1, '工程造价': 8, '有限责任': 8, '分公司': 42, '土地': 51, '中铭': 1, '湖南省': 14, '司法鉴定': 12, '金源': 3, '融达': 1, '税务师': 4, '组成': 19, '经营范围': 4, '审计': 63, '含': 42, '上市公司': 43, '土地规划': 6, '单项': 3, '税务': 13, 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1, '3012': 1, '七里河区': 1, '诊疗': 9, '清理': 21, '质疑': 7, '数据录入': 17, '病历': 2, '转行': 3, '参与者': 5, '测试报告': 14, '转化成': 9, 'NAN': 556, '组成员': 6, '守时': 5, '信守': 1, '承诺': 16, 'ISO27001': 2, 'ISO20000': 1, '内审员': 2, 'ITIL': 1, 'FOUNDATION': 1, '打单': 1, '门户开放': 1, '科众': 1, '积淀': 2, '商机': 17, '商务谈判': 38, '签署': 13, '商业机会': 6, '英美': 5, '打交道': 9, '对此': 3, '表现出色': 2, '顶层': 1, '架构设计': 65, '规划设计': 15, 'EXECEL': 1, '自如': 16, 'ANALYTICS': 7, '切实可行': 15, '个人简历': 4, '负责管理': 12, '模式分析': 2, '总体设计': 2, '转型': 44, 'EA': 1, '口才': 2, '全局观念': 2, '管理软件': 11, 'WMS': 12, 'MES': 21, '超额': 9, '721': 5, '职场': 19, '70%': 11, '论坛': 24, '沙盘': 3, '更稳': 4, '→': 44, '组长': 35, '要性': 2, '更迭': 4, '输入输出': 5, '进取精神': 15, '杭州': 59, '地级市': 4, '需能': 6, '工作调动': 2, '渠道商': 6, '铁三角': 2, '主营业务': 7, '无线': 11, '与物': 2, '交换': 30, '事关': 4, '人民': 3, '群众': 3, '明确提出': 2, '高度重视': 2, '如今': 3, '军队': 5, '新兴': 8, '强劲': 2, '拉动': 2, '品牌优势': 3, '信服': 9, '科技股份': 6, '企业级': 17, '基础架构': 19, '智': 8, '三大': 6, 'IDC': 17, 'VPN': 13, '全网': 2, '下一代': 3, '防火墙': 50, 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'分工': 5, '办': 4, '归集': 8, '数据共享': 4, '链家': 74, '就近': 13, '链家网': 12, '贝壳': 18, '网获客': 1, 'LINK': 5, '房源': 35, '利弊': 1, '邀约': 21, '购房': 10, '展位': 12, '为什么': 4, '我会': 2, '首先': 7, '晋升制度': 11, '其次': 5, '提佣': 4, '提点': 3, '大力': 1, '提拔': 7, '无论': 7, '一技之长': 3, '容身之处': 1, '无人': 5, '能比': 1, '开单': 3, '共': 10, '包容': 8, '有家': 2, '温暖': 6, '觉得': 5, '安心': 9, '有责': 2, '是不是': 4, '累': 3, '外出': 30, '行程': 4, '单纯': 9, '坐': 2, '相对': 22, '打电话': 9, 'NO': 1, '提倡': 8, '取消': 1, '洗盘': 1, '接下来': 1, '社区': 40, '什么样': 12, '分为': 10, '两条路': 1, '顾名思义': 1, '省心': 3, '两耳不闻': 1, '窗外事': 1, '一心': 1, '只': 30, '卖房子': 1, '提': 9, '可观': 3, '年入': 3, '几十万': 1, '店长': 7, '大部': 2, '90': 23, '三分之一': 1, '够快': 1, '异地': 11, '调转': 1, '回老家': 1, '保': 8, '体面': 2, '一席之地': 1, '看到': 6, '依然': 2, '困惑': 2, '茫然': 1, '聊天': 7, '帮忙': 2, '人均': 5, '700': 3, '1500': 9, '双人间': 2, '自荐': 3, '25000': 3, '45000': 1, '销售额': 22, '得': 23, '大区': 15, '叠加': 1, '出国游': 5, '店面': 6, '试用': 14, '实训': 15, '宣讲会': 2, '请勿': 11, '成人教育': 1, '计算机操作': 13, '基本保障': 4, '每周三': 4, 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'塔楼': 1, '3808': 1, '城星路': 1, '脑图': 1, '之外': 5, '机械制造': 4, '组装': 14, '离散': 1, 'OPC': 2, 'PLC': 12, 'DCS': 1, 'PCS': 1, '仅': 10, 'SERVICES': 3, 'MAINTENANCE': 2, 'ENHANCEMENT': 1, 'PACKAGES': 1, 'APPLICATIONS': 1, 'UTILIZING': 1, 'ACCOMPANYING': 1, 'ENVIRONMENTS': 1, '开发工具': 25, 'MODULES': 1, 'DEMAND': 1, 'OPTIMIZATION': 3, 'BLUEPRINT': 1, 'DESIGN': 2, 'PROGRAM': 4, 'LINE': 1, '系统配置': 11, 'NECESSARY': 1, 'APPLY': 1, 'CORRESPONDING': 1, 'ABEAM': 2, 'METHOD': 1, 'ENTERPRISE': 1, 'DIGITAL': 3, 'TRANSFORMATION': 6, 'MEMBERS': 2, 'PLANNING': 1, 'INDUSTRIAL': 1, 'ESTABLISHING': 1, 'DELIVER': 1, 'DIVERSIFIED': 1, 'COMPREHENSIVE': 1, 'NOT': 1, 'LIMITED': 1, 'EPR': 3, 'STRATEGIC': 1, 'VALUE': 2, 'SATISFACTION': 1, 'JAPANESE': 1, 'ANALYTIC': 1, 'CAPABILITY': 2, 'HIGHLY': 1, 'MOTIVATED': 1, '主观': 11, 'DESIRE': 1, 'PURSUING': 1, 'PERSONAL': 1, 'EFFECTIVELY': 3, 'DIVERSE': 1, 'GROUP': 2, 'INDIVIDUALS': 1, 'AT': 5, 'LEVELS': 1, 'ORGANIZATION': 1, 'FLEXIBLE': 1, 'WILLING': 3, 'TRAVEL': 2, 'TAKE': 2, 'CHALLENGE': 1, 'ENTHUSIASM': 1, 'SALESFORCE': 2, 'SERVICENOW': 2, 'MAGENTO': 1, 'O': 12, '信管': 3, 'TCET': 1, 't2025': 1, '故': 1, 'OFFER': 16, '线会': 3, '140': 4, '豁达': 1, '富': 2, '环卫': 4, '提议': 2, '随机应变': 5, '部级': 1, '战略目标': 4, '洞悉': 6, 'IPD': 1, 'OA': 11, 'PLM': 2, 'SRM': 3, '制定方案': 1, '星空': 5, '易飞': 1, 'E10': 1, '速记': 1, '电销': 4, '济南': 13, '操作系统': 175, '试运行': 4, '所用': 1, '企业财务': 4, '鞋服': 1, '国外软件': 1, 'NC': 1, 'EAS': 1, '浪潮': 8, '演练': 7, '压降': 1, '防御': 5, '加固': 12, 'SOAR': 2, '及运维': 14, 'WIRESHARK': 3, 'BURP': 1, 'FIDDLER': 1, 'SECURITY': 4, '四地': 1, '可选': 1, 'YUNHONG': 1, 'INSIGHT': 1, '996': 3, '影刀': 1, 'RPA': 3, '切换': 5, '降本增效': 1, '改': 4, '调配': 16, '论述': 1, '面访': 1, '复核': 16, '英语系': 1, '推荐信': 2, '成功率': 2, '英语考试': 1, '获奖': 8, '荣誉证书': 1, '泛微': 3, '巡检': 52, '维拉': 1, '技术水平': 8, '总体规划': 3, '体制': 2, 'OKR': 1, '北上': 1, '广深': 1, '超产奖': 1, '每餐': 2, '荤': 1, '一素': 1, '一汤': 2, '自选': 1, '桌球': 3, '呼和浩特市': 1, '近照': 1, '身份证': 2, '来函': 1, '初选': 1, '仅招': 1, '引申': 1, '烟草': 4, '公募': 4, '托管': 6, '投资银行': 1, '约束': 3, '财资': 1, '软件产品': 45, '转账': 1, '汇款': 2, '清算': 2, 'WEBLOGIC': 6, 'TOMCAT': 21, '中间件': 32, '做人': 1, '以德': 1, '为先': 4, '次之': 2, '明理': 1, '央企': 2, '任君': 1, '七七八八': 1, '封底': 1, '有志': 15, '青年': 8, '金融业务': 6, '约见': 2, '个人特长': 1, '功能测试': 6, '一类': 4, '表及': 1, '届校': 4, '工作组': 1, '分派': 3, 'GL': 3, 'AP': 2, '二个': 1, '所在区域': 4, '高顿': 7, '前程无忧': 1, '深化': 4, '平均年龄': 2, '无数': 1, '50000': 4, '创': 3, '造就': 1, '不凡': 2, '保研': 2, '咨': 1, '考公等': 1, '就读': 1, '英国剑桥大学': 1, '美国加州大学伯克利分校': 1, '牛津大学': 1, '趣味': 5, '训练营': 3, '麦田': 1, '战狼营': 1, '侠客行': 1, '轨迹': 9, '房产': 10, '平均工资': 2, '互助金': 2, '重疾': 3, '人民大会堂': 2, '数科': 1, '反馈系统': 1, '择优录用': 1, '西班牙语': 9, '深谙': 4, '详谈': 5, '销售市场': 4, '主投': 1, '字节': 2, '时不时': 1, '闪现': 1, '脑洞': 4, '友情链接': 1, '商务礼品': 2, '礼品公司': 1, '长尾': 1, '词': 2, '里面': 2, '网盟': 2, '定向': 10, '降低成本': 8, '老师傅': 1, '短剧': 7, '1%': 4, '轻轻松松': 2, '你够': 1, '评优': 3, '电影': 3, '歌': 2, '好玩': 2, '元旦': 5, '这种': 3, '呢': 8, '坚决贯彻': 1, '想学': 2, '没错': 2, '三遍': 2, '以高': 1, '曝光': 8, '计划成本': 1, '调节': 3, '测出': 1, '高出': 1, '点击率': 4, '喜': 1, '月耗': 1, '广告主': 4, '投流': 1, '网络应用': 2, '脑暴': 2, '制表': 2, '起量': 1, '重新制定': 1, '可息流': 1, '愉快': 9, '踏踏实实': 1, '搞钱': 1, 'AMS': 1, '控等': 1, '玩游戏': 1, '对手': 1, '小说': 5, '回报率': 4, '日耗': 2, '10w': 1, '自产剧': 1, '多渠道': 7, '剧': 1, '58': 1, '同城': 1, '美团': 15, '线索': 11, '动作': 6, '巨量': 7, '精力充沛': 4, '达到最佳': 1, 'AE': 1, '有航': 1, '旅类': 1, '乙方': 3, '兼备': 2, '二类': 1, '如拼': 1, 'CID': 1, '磁力': 2, '盈利': 15, '剪辑': 16, '视频剪辑': 8, '各项任务': 7, '高压': 5, '直客': 2, '科学管理': 2, '充值': 1, '不足': 5, '损失': 3, '食品类': 3, '目': 3, '金牛': 1, '带货类': 1, '页': 4, '对抖音': 4, '功效': 2, '300W': 1, '够': 3, '舒畅': 1, '双月': 1, '俱全': 2, '满勤': 4, '.....................................................................................................................': 1, '四通八达': 1, '旁': 5, '湖里区': 2, '红豆杉': 1, '1003': 1, '婚恋': 1, '国短': 1, '进线量': 1, '起成': 1, '不断扩大': 1, '控度': 1, '前行': 3, '距离': 5, '亦庄': 2, '荣京': 1, '明显': 4, '号召': 1, 'UC': 1, '攥': 2, '损益': 2, '预估': 8, '广告学': 1, '旺盛': 1, '死': 1, '磕': 1, '成人': 1, '选取': 5, '大胆': 7, '透过': 3, '有千川': 1, '爱学': 2, '存取': 1, '早会': 1, '周会': 1, '资管': 2, '投行部': 1, '销售业务': 7, '场外': 1, '荣信': 1, '华夏': 2, '矿权': 1, 'www.88': 1, 'cc88': 1, '2003': 1, '矿产': 2, '因本': 1, '特聘': 2, '有时': 2, '武侯区': 1, '二环路': 1, '号祥福苑': 1, '内审': 1, '商业银行': 1, '若干': 4, '共性': 1, 'OWNER': 5, '固化': 2, '实物': 2, '数据流': 5, 'LTO': 1, 'OTC': 1, 'SS': 1, '接手': 1, '商用车': 11, '保险产品': 1, '车险': 2, '限等': 3, '往来': 12, '立案': 3, '缴费': 6, '时限': 8, '官文': 2, '转达': 12, '代教': 2, '办公时间': 3, '行动': 11, '万众': 1, '专代': 1, '预料': 1, '力量雄厚': 1, '一揽子': 2, '专利局': 4, '审查员': 2, '责任人': 8, '推向': 1, '通知书': 4, 'TEM': 3, '前十': 1, '辖': 23, '逻辑学': 1, '推动力': 6, '每逢': 2, '明亮': 3, '饮水机': 3, '友善': 11, '训战': 2, '入企': 2, '打通': 4, '任督': 2, '二脉': 2, '不止': 2, '真诚相待': 2, '共同进步': 8, '中转': 1, '转场': 1, '手法': 1, '文职类': 2, '涉外': 9, '发文': 2, '查文': 1, '年费': 5, '商定': 1, '日历': 1, '发票': 7, '财务报告': 1, '租金': 12, '平面': 8, '应付账款': 1, '账龄': 2, '全勤奖金': 1, '基数': 4, '最长': 1, '六个月': 3, '交官': 1, '程序性': 1, '信函': 16, '优审': 2, '相同': 4, '广州市': 13, '黄埔区': 1, '期限': 10, '国家知识产权局': 1, '流转': 9, '案卷': 2, '收': 4, '发和': 1, '光': 9, '重新': 3, '稳步': 2, '权责': 1, '预审': 5, '快维': 1, '盖章': 1, '外观': 7, '核稿': 2, '送案': 1, '券': 6, '日常事务': 9, '建案': 1, '集美': 3, '软件园': 5, '三期': 3, '时能': 3, '反映': 11, '商标': 18, '转函': 1, '日到': 1, '业务主管': 3, '韩语': 12, 'ADOBE': 1, '薪金': 3, '光华': 6, '核心区': 1, '宽容': 1, '天道酬勤': 2, '补正': 3, 'EAC': 2, '涉外工作': 1, '挂证': 1, '卷宗': 2, '重复性': 3, '操作性': 1, '外内': 1, '连': 6, '长安街': 1, '高档': 1, '英语角': 2, '读书': 4, '辩论会': 1, '讨论会': 1, '牛奶': 1, '置装': 1, '加盟': 9, '拳拳': 1, '之意': 1, '赏识': 1, '器重': 1, '兴奋': 1, '家园': 2, '六级': 11, '彼速': 1, 'S4': 1, '票据': 2, '温州': 2, '收文': 1, '官费': 1, '文秘': 1, '派对': 2, '可往': 1, '年青人': 1, '内行': 2, '易': 5, '苏权': 7, '硕': 1, '南京航空航天大学': 2, '东南大学': 2, '南京大学': 1, '河海大学': 1, '山东大学': 1, '贵州大学': 1, '上海理工大学': 1, '火箭': 1, '军': 1, '院': 2, '电科院': 1, '金陵': 1, '科研院所': 4, '华能': 3, '中兴通讯': 1, '国电': 1, '南瑞': 2, '中孚': 1, '数智源': 1, '扬子': 2, '及国央企': 1, '四家': 2, '众': 2, '达德': 2, '华成': 3, '权博': 2, '多地': 2, '科技局': 1, '冶金': 6, '多名': 1, '为原': 1, 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'EUREKA': 1, 'APOLLO': 1, '类新': 1, '精研': 1, 'LABVIEW': 1, 'SIGHT': 1, 'EXPLORER': 1, 'VISIONMASTER': 1, '7X24': 1, '全天候': 1, '待命': 1, '以备': 1, '持续时间': 1, '重新安排': 1, '加急': 1, '全球性': 1, 'ITSM': 1, 'REMEDY': 1, '澄清': 1, 'RHCSA': 1, '运维外': 1, '责向': 1, '罗克佳华': 1, '华西': 1, '本院': 1, '分院': 1, '查房': 1, '院区': 1, '搬迁': 2, '值夜班': 1, '故障率': 2, '完好率': 1, '正确处理': 1, '化解': 1, '拖拉': 1, '院方': 1, 'server2003': 1, '组串': 1, '人身': 1, '意外保险': 1, '几千元': 1, '几万元': 1, '有入': 1, '有饭': 1, '数据库安全': 6, 'QMS': 1, '端运维': 1, '公安局': 1, '无事故': 1, '台式机': 1, '流媒体': 1, '人因': 1, '生物力学': 1, '转业军人': 1, '数天': 1, '关系融洽': 1, '普遍存在': 1, '不扣': 1, '长薪': 1, '舒心': 1, '有识之士': 1, '固定式': 1, '码器': 1, 'HELPDESK': 2, '充电站': 3, '疑难解答': 1, '具体分析': 1, 'TCUDUMP': 1, 'MESH': 1, '注册软件': 1, '账套': 2, '飞行高度': 1, '速度限制': 1, '适飞': 1, '空域': 2, '实名': 1, '降落': 1, '低空飞行': 1, 'CAAC': 1, '中铁': 1, '中交': 1, '校核': 1, '引导性': 1, '防雷': 1, '加速器': 5, '何种': 1, '核工程': 1, '岗责': 1, 'MQ': 3, '台州市': 1, 'RAID': 1, '独自': 2, '沉着': 1, '公安系统': 1, '精密仪器': 1, '驾驶执照': 1, '夜': 1, 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1, 'RABBIRMQ': 1, '复测': 1, '受限制': 1, '攻坚': 1, '天网': 1, '卡口': 1, '早晨': 1, '系统启动': 1, '及盘': 2, '介质': 1, '完整性检查': 1, '时段': 1, '存贮': 1, '刚入': 1, '水路': 1, '气路': 1, '油路': 1, '液压': 1, '射线': 3, '断电': 1, '出束': 1, '差不多': 1, '三次': 2, '通后': 1, '没空': 1, '前一天': 1, '基线': 1, '分级分类': 1, '过中': 1, 'CICD': 1, '具体操作': 1, '护网': 1, '以域': 1, '内销': 1, '后运维': 1, '延庆': 1, '康庄': 1, '连线': 1, 'VOIP': 2, 'IAD': 1, '话机': 1, '中继': 1, 'VOS': 1, 'SIPROUTE': 1, 'SBC': 1, '软交换': 1, '30B': 1, '服务器上安装': 1, 'DEBIAN': 1, 'RS485': 1, '20mA': 1, '接线': 1, 'DI': 1, '规约': 1, '电流表': 1, '大同': 2, '被迫': 1, '才子': 1, '共筑': 1, '长城': 1, '机房设备': 1, '懂电': 1, '党政机关': 1, '传版': 2, '小站': 2, '如遇': 1, '追求进步': 1, '满溢': 1, '足': 1, '放养': 1, '业余活动': 1, 'DZQZ': 1, '后浪': 1, '领用': 2, '代办': 1, '优才': 1, '品行不端': 1, '捷通': 1, '满年': 1, '隔周': 1, '茂名市': 1, '油城': 1, '九路': 1, '大院': 1, '路建名': 1, '宅': 1, '花园式': 1, '集中管理': 1, '里程碑': 1, '操作员': 1, '投运': 1, '污水': 1, '处理厂': 1, 'AAO': 1, '客服热线': 1, '建置': 1, '提前准备': 1, '晕车': 1, '海洋环境': 1, '山石': 1, 'SPUG': 1, '黑白': 1, '限流': 1, '内网': 1, '机设备': 3, '上门服务': 3, '服务态度': 4, '周边城市': 3, '潍坊市': 2, 'HCS': 1, '软调': 1, '2288': 1, '实施规划': 1, '存储器': 1, '国产化': 1, '台式': 1, '总结性': 1, '肩负': 1, '驻地': 1, '南岸': 1, '蜀南': 1, '安全可靠': 1, '值班人员': 1, '伴': 1, '创云': 1, '固件': 1, 'DOS': 1, '游戏类': 1, '网吧': 1, '机有': 1, '驻派': 1, '用户量': 1, '闽南语': 1, '顺义': 1, '抽样调查': 1, '诊所': 1, '医科大学': 1, '膏方': 1, '胃病': 1, '内科': 1, '高等': 1, '遗产': 1, '考古': 1, 'POTOSHOP': 1, '居于': 1, '人机': 1, '对国': 1, '省控': 1, '区县': 1, '曲江': 1}\n"
     ]
    }
   ],
   "source": [
    "# 分词之后是⼀个list\n",
    "# 假设 word 就是分词后得到的列表，stopwords 是停用词列表\n",
    "word_tf = {}\n",
    "# 先将所有单词统一转换为大写，方便后续不区分大小写统计词频\n",
    "upper_word = [w.upper() if w.isalpha() else w for w in word]\n",
    "for w in upper_word:\n",
    "    if w not in word_tf.keys():\n",
    "        word_tf[w] = 1\n",
    "    else:\n",
    "        word_tf[w] += 1\n",
    "\n",
    "# 除去停⽤词\n",
    "for sw in stopwords:\n",
    "    sw_upper = sw.upper()  # 将停用词也转换为大写，确保匹配\n",
    "    if sw_upper in word_tf.keys():\n",
    "        word_tf.pop(sw_upper)\n",
    "word_tf.pop(' ') \n",
    "print(word_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b5befb90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'13.30'), (1, '13.00'), (1, '12c'), (1, '12W'), (1, '1260'), (1, '123'), (1, '1214'), (1, '1200SCL'), (1, '1200'), (1, '11k'), (1, '11W'), (1, '11G'), (1, '1175'), (1, '1168'), (1, '115'), (1, '112'), (1, '1100'), (1, '11.30'), (1, '10w'), (1, '10h'), (1, '10W'), (1, '106'), (1, '105'), (1, '10400'), (1, '103'), (1, '102'), (1, '101'), (1, '10086'), (1, '1003'), (1, '10.8'), (1, '10.723'), (1, '10.383'), (1, '10.3%'), (1, '1.9'), (1, '1.7'), (1, '1.6'), (1, '1.5%'), (1, '1.24'), (1, '1.22'), (1, '1.18'), (1, '0ffice'), (1, '076086222811'), (1, '03678'), (1, '03'), (1, '02'), (1, '00.14'), (1, '0.5'), (1, '.....................................................................................................................'), (1, '.......'), (1, '----------------------------------------------------------------------------------------------------'), (1, '------------------------------------------------------------'), (1, '---------------------------------------------------'), (1, '--------------------------------------------'), (1, '-------------------------------------------'), (1, '-------------------------------'), (1, '-----------------------------'), (1, '-----------------'), (1, '-----'), (1, '++')]\n"
     ]
    }
   ],
   "source": [
    "#下⾯是让字典按照频率排序\n",
    "Zip=zip(word_tf.values(),word_tf.keys())\n",
    "order=list(Zip)\n",
    "order.sort(reverse=True)\n",
    "# 排序之后是⼀个list，元素是tuple（词,频数)，可以打印出来看看\n",
    "print(order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6cb5dbe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "word_name = []\n",
    "word_num = []\n",
    "for i in range(50):\n",
    "    word_name.append(order[i][1])\n",
    "    word_num.append(order[i][0])\n",
    "\n",
    "# 设置柱状图的颜色，可以选择合适的颜色列表，这里示例用蓝色系列\n",
    "bar_colors = ['#1f77b4' for _ in range(50)]\n",
    "\n",
    "# 下面是作出词语的频数柱状图\n",
    "plt.figure(figsize=(13, 5))\n",
    "# 设置柱状图颜色并绘制\n",
    "bars = plt.bar(range(50), word_num, color=bar_colors)\n",
    "\n",
    "# 设置x轴刻度标签为词名，并旋转90度，同时调整字体大小使显示更美观\n",
    "plt.xticks(range(50), word_name, rotation=90, fontsize=10)\n",
    "# 设置中文字体为黑体，解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# 添加标题和坐标轴标签，使其表意更清晰\n",
    "plt.title('词语频数统计', fontsize=16)\n",
    "plt.xlabel('词语', fontsize=12)\n",
    "plt.ylabel('频数', fontsize=12)\n",
    "\n",
    "# 添加网格线，增强图表可读性，设置线条样式、颜色和透明度\n",
    "plt.grid(axis='y', linestyle='--', color='gray', alpha=0.7)\n",
    "\n",
    "# 可以添加图例（如果有需要，这里示例简单设置一下图例说明，可根据实际情况调整）\n",
    "plt.legend([bars], ['频数'], loc='upper right', fontsize=10)\n",
    "\n",
    "# 调整图表布局，避免标签显示不全等问题\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfd2ac0",
   "metadata": {},
   "source": [
    "## 关键词统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64e805fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 名称                              关键词_all\n",
      "0             数据分析师                        数据处理,数据库,数据挖掘\n",
      "1             数据分析师  全职工作,教育,数学/统计相关专业,数据分析/挖掘经验,智能硬件学习机\n",
      "2             数据分析师     教育相关,数学/统计相关专业,数据分析/挖掘经验,计算机相关专业\n",
      "3         数据分析师（校招）      Python,R,SAS,数据分析/挖掘经验,机器学习建模经验\n",
      "4     数据分析师(J10073)                          Python,SPSS\n",
      "...             ...                                  ...\n",
      "7648          数据分析师    数学/统计相关专业,数据分析/挖掘经验,电商/零售行业数据相关经验\n",
      "7652        量化数据研究员                               Python\n",
      "7661         数据分析专员               Python,R,SQL,数学/统计相关专业\n",
      "7663         证券数据分析           证券数据归纳分析,金融行业数据相关经验,风控相关经验\n",
      "7664        电商数据分析师                               Python\n",
      "\n",
      "[4560 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df3=df2.copy()\n",
    "# 先将所有的关键词列替换为空字符串，而不是 NaN\n",
    "df3[['关键词1', '关键词2', '关键词3', '关键词4', '关键词5']] = df3[['关键词1', '关键词2', '关键词3', '关键词4', '关键词5']].fillna('')\n",
    "\n",
    "# 合并关键词列，此时 NaN 值已经被替换为空字符串，所以不会包含在合并后的字符串中\n",
    "# 同时过滤掉空字符串，确保不包含空值\n",
    "df3['关键词_all'] = df3[['关键词1', '关键词2', '关键词3', '关键词4', '关键词5']].apply(\n",
    "    lambda row: ','.join(sorted(set([key for key in row if key != '']))), axis=1)  # 使用set去除重复关键词，并排序\n",
    "\n",
    "# 清理合并后的关键词列，去除前后的逗号，并去除空字符串（虽然前面已经处理过，但这里再次确认）\n",
    "df3['关键词_all'] = df3['关键词_all'].str.strip(',')\n",
    "\n",
    "# 如果需要进一步分割关键词列，以便进行后续分析（例如，计算每个关键词的出现频率）\n",
    "# 可以使用str.split方法将关键词列分割为列表，但注意这将返回一个对象类型的列\n",
    "df3['关键词_list'] = df3['关键词_all'].str.split(',')\n",
    "\n",
    "# 显示处理后的DataFrame\n",
    "print(df3[['名称', '关键词_all']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33454850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python           527\n",
       "计算机相关专业          451\n",
       "SQL              438\n",
       "数学/统计相关专业        422\n",
       "数据分析/挖掘经验        350\n",
       "数据分析             243\n",
       "线下教育             164\n",
       "Java             153\n",
       "SPSS             129\n",
       "小班教学（3-25人）      121\n",
       "Excel            114\n",
       "坐班               114\n",
       "系统运维             114\n",
       "全职                96\n",
       "大数据               95\n",
       "R                 88\n",
       "招聘与配置             86\n",
       "MySQL             79\n",
       "经济/工商管理相关专业       76\n",
       "Linux             74\n",
       "学前儿童/小学生          72\n",
       "英语可用于工作           69\n",
       "金融行业数据相关经验        67\n",
       "数据挖掘              65\n",
       "不接受居家办公           64\n",
       "大数据开发经验           64\n",
       "数据分析能力            63\n",
       "Power BI          58\n",
       "有外贸工作经验           56\n",
       "国际贸易专业            56\n",
       "年终奖               55\n",
       "网络运维              54\n",
       "培训机构              54\n",
       "Tableau           53\n",
       "需求分析              52\n",
       "Oracle            51\n",
       "Hive              51\n",
       "中学生               51\n",
       "数据处理              51\n",
       "五险一金              50\n",
       "SAS               50\n",
       "国内咨询机构            50\n",
       "Spark             49\n",
       "电商/零售行业数据相关经验     49\n",
       "市场调研              47\n",
       "C++               43\n",
       "人力/财务/行政/法务       43\n",
       "数据库               43\n",
       "相关分析              42\n",
       "B端产品              41\n",
       "Name: 关键词_all, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords_series = df3['关键词_all'].str.split(',').explode()\n",
    "\n",
    "# 计算每个关键词的出现次数\n",
    "keyword_counts = keywords_series.value_counts()\n",
    "\n",
    "# 显示按出现次数排序的前50个关键词\n",
    "top_keywords = keyword_counts.head(50)\n",
    "top_keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4794150",
   "metadata": {},
   "source": [
    "## 公司规模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "abe5c8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>名称</th>\n",
       "      <th>薪水</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>描述</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>...</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>要求</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>城市</th>\n",
       "      <th>地区</th>\n",
       "      <th>关键词_all</th>\n",
       "      <th>关键词_list</th>\n",
       "      <th>公司规模2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>数据处理,数据库,数据挖掘</td>\n",
       "      <td>[数据处理, 数据库, 数据挖掘]</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>...</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>安阳</td>\n",
       "      <td>文峰区</td>\n",
       "      <td>全职工作,教育,数学/统计相关专业,数据分析/挖掘经验,智能硬件学习机</td>\n",
       "      <td>[全职工作, 教育, 数学/统计相关专业, 数据分析/挖掘经验, 智能硬件学习机]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>随州</td>\n",
       "      <td>曾都区</td>\n",
       "      <td>教育相关,数学/统计相关专业,数据分析/挖掘经验,计算机相关专业</td>\n",
       "      <td>[教育相关, 数学/统计相关专业, 数据分析/挖掘经验, 计算机相关专业]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>大数据</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td>需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>成都</td>\n",
       "      <td>双流区</td>\n",
       "      <td>Python,R,SAS,数据分析/挖掘经验,机器学习建模经验</td>\n",
       "      <td>[Python, R, SAS, 数据分析/挖掘经验, 机器学习建模经验]</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...</td>\n",
       "      <td>新闻/出版</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1000-9999</td>\n",
       "      <td>郑州</td>\n",
       "      <td>郑州</td>\n",
       "      <td>Python,SPSS</td>\n",
       "      <td>[Python, SPSS]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>杭州超级伙伴科技</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>电商/零售行业数据相关经验</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>...</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>100-499</td>\n",
       "      <td>绍兴</td>\n",
       "      <td>柯桥区</td>\n",
       "      <td>数学/统计相关专业,数据分析/挖掘经验,电商/零售行业数据相关经验</td>\n",
       "      <td>[数学/统计相关专业, 数据分析/挖掘经验, 电商/零售行业数据相关经验]</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>吾执投资</td>\n",
       "      <td>量化数据研究员</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>五险一金，零食下午茶，带薪年假，包吃</td>\n",
       "      <td>基金</td>\n",
       "      <td>Python</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td>职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>上海</td>\n",
       "      <td>浦东新区</td>\n",
       "      <td>Python</td>\n",
       "      <td>[Python]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>大数据</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>...</td>\n",
       "      <td>SQL</td>\n",
       "      <td>能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>昆明</td>\n",
       "      <td>五华区</td>\n",
       "      <td>Python,R,SQL,数学/统计相关专业</td>\n",
       "      <td>[Python, R, SQL, 数学/统计相关专业]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>NaN</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td>...</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>20-99</td>\n",
       "      <td>西安</td>\n",
       "      <td>雁塔区</td>\n",
       "      <td>证券数据归纳分析,金融行业数据相关经验,风控相关经验</td>\n",
       "      <td>[证券数据归纳分析, 金融行业数据相关经验, 风控相关经验]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>Python</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td>负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>20-99</td>\n",
       "      <td>徐州</td>\n",
       "      <td>云龙区</td>\n",
       "      <td>Python</td>\n",
       "      <td>[Python]</td>\n",
       "      <td>小</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4560 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              标题             名称       薪水   经历要求 学历要求  \\\n",
       "0           东方优特          数据分析师  10000.0   经验不限   本科   \n",
       "1       松鼠Ai智能学习          数据分析师   5500.0   经验不限   本科   \n",
       "2          羽山机器人          数据分析师   5500.0   经验不限   本科   \n",
       "3        麦可思数据科技      数据分析师（校招）   8500.0  在校/应届   本科   \n",
       "4           天星教育  数据分析师(J10073)   7000.0   经验不限   本科   \n",
       "...          ...            ...      ...    ...  ...   \n",
       "7648    杭州超级伙伴科技          数据分析师   7000.0   经验不限   本科   \n",
       "7652        吾执投资        量化数据研究员  10500.0   经验不限   本科   \n",
       "7661          洲宇         数据分析专员   5000.0   经验不限   本科   \n",
       "7663  西安易升电子有限公司         证券数据分析   7500.0   经验不限   本科   \n",
       "7664       江苏蕲臻堂        电商数据分析师   6000.0   经验不限   本科   \n",
       "\n",
       "                                                     描述         工作内容  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶         在线教育   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...         智能硬件   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金         人工智能   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...          大数据   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...        新闻/出版   \n",
       "...                                                 ...          ...   \n",
       "7648  五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖         电子商务   \n",
       "7652                                 五险一金，零食下午茶，带薪年假，包吃           基金   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假          大数据   \n",
       "7663                                                NaN  电子/半导体/集成电路   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游        批发/零售   \n",
       "\n",
       "            关键词1           关键词2       关键词3  ...        关键词5  \\\n",
       "0           数据处理            数据库       数据挖掘  ...               \n",
       "1      数学/统计相关专业      数据分析/挖掘经验    智能硬件学习机  ...        全职工作   \n",
       "2      数学/统计相关专业        计算机相关专业  数据分析/挖掘经验  ...               \n",
       "3       机器学习建模经验              R        SAS  ...      Python   \n",
       "4         Python           SPSS             ...               \n",
       "...          ...            ...        ...  ...         ...   \n",
       "7648   数学/统计相关专业  电商/零售行业数据相关经验  数据分析/挖掘经验  ...   数学/统计相关专业   \n",
       "7652      Python                            ...      Python   \n",
       "7661         SQL         Python          R  ...         SQL   \n",
       "7663  金融行业数据相关经验         风控相关经验   证券数据归纳分析  ...  金融行业数据相关经验   \n",
       "7664      Python                            ...      Python   \n",
       "\n",
       "                                                     要求   薪水最小值    薪水最大值  \\\n",
       "0     业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...  8000.0  12000.0   \n",
       "1     经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...  3000.0   8000.0   \n",
       "2     深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...  3000.0   8000.0   \n",
       "3     需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...  7000.0  10000.0   \n",
       "4     负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...  6000.0   8000.0   \n",
       "...                                                 ...     ...      ...   \n",
       "7648  负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...  6000.0   8000.0   \n",
       "7652  职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...  8000.0  13000.0   \n",
       "7661  能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...  4000.0   6000.0   \n",
       "7663  职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...  5000.0  10000.0   \n",
       "7664  负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...  5000.0   7000.0   \n",
       "\n",
       "           公司规模  城市    地区                              关键词_all  \\\n",
       "0       100-499  北京   海淀区                        数据处理,数据库,数据挖掘   \n",
       "1          0-20  安阳   文峰区  全职工作,教育,数学/统计相关专业,数据分析/挖掘经验,智能硬件学习机   \n",
       "2          0-20  随州   曾都区     教育相关,数学/统计相关专业,数据分析/挖掘经验,计算机相关专业   \n",
       "3       100-499  成都   双流区      Python,R,SAS,数据分析/挖掘经验,机器学习建模经验   \n",
       "4     1000-9999  郑州    郑州                          Python,SPSS   \n",
       "...         ...  ..   ...                                  ...   \n",
       "7648    100-499  绍兴   柯桥区    数学/统计相关专业,数据分析/挖掘经验,电商/零售行业数据相关经验   \n",
       "7652       0-20  上海  浦东新区                               Python   \n",
       "7661       0-20  昆明   五华区               Python,R,SQL,数学/统计相关专业   \n",
       "7663      20-99  西安   雁塔区           证券数据归纳分析,金融行业数据相关经验,风控相关经验   \n",
       "7664      20-99  徐州   云龙区                               Python   \n",
       "\n",
       "                                       关键词_list 公司规模2  \n",
       "0                             [数据处理, 数据库, 数据挖掘]     中  \n",
       "1     [全职工作, 教育, 数学/统计相关专业, 数据分析/挖掘经验, 智能硬件学习机]     小  \n",
       "2         [教育相关, 数学/统计相关专业, 数据分析/挖掘经验, 计算机相关专业]     小  \n",
       "3         [Python, R, SAS, 数据分析/挖掘经验, 机器学习建模经验]     中  \n",
       "4                                [Python, SPSS]     小  \n",
       "...                                         ...   ...  \n",
       "7648      [数学/统计相关专业, 数据分析/挖掘经验, 电商/零售行业数据相关经验]     中  \n",
       "7652                                   [Python]     小  \n",
       "7661                [Python, R, SQL, 数学/统计相关专业]     小  \n",
       "7663             [证券数据归纳分析, 金融行业数据相关经验, 风控相关经验]     小  \n",
       "7664                                   [Python]     小  \n",
       "\n",
       "[4560 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3.copy()\n",
    "def classify_company_size(size):\n",
    "    if '0-99' in size or '20-99' in size or '0-20' in size:\n",
    "        return '小'\n",
    "    elif '100-499' in size or '100-999' in size:\n",
    "        return '中'\n",
    "    elif '1000-9999' in size or '1000+' in size:\n",
    "        return '大'\n",
    "    else:\n",
    "        return '未知'\n",
    "\n",
    "# 应用函数到“公司规模”列，创建新列“公司规模2”\n",
    "df4['公司规模2'] = df4['公司规模'].apply(classify_company_size)\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a529222d",
   "metadata": {},
   "source": [
    "## 数据调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f641b9c0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司名称</th>\n",
       "      <th>名称</th>\n",
       "      <th>城市</th>\n",
       "      <th>地区</th>\n",
       "      <th>薪水</th>\n",
       "      <th>薪水最小值</th>\n",
       "      <th>薪水最大值</th>\n",
       "      <th>经历要求</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>...</th>\n",
       "      <th>关键词1</th>\n",
       "      <th>关键词2</th>\n",
       "      <th>关键词3</th>\n",
       "      <th>关键词4</th>\n",
       "      <th>关键词5</th>\n",
       "      <th>关键词_all</th>\n",
       "      <th>关键词_list</th>\n",
       "      <th>工作内容</th>\n",
       "      <th>描述</th>\n",
       "      <th>要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东方优特</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>100-499</td>\n",
       "      <td>...</td>\n",
       "      <td>数据处理</td>\n",
       "      <td>数据库</td>\n",
       "      <td>数据挖掘</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>数据处理,数据库,数据挖掘</td>\n",
       "      <td>[数据处理, 数据库, 数据挖掘]</td>\n",
       "      <td>在线教育</td>\n",
       "      <td>五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶</td>\n",
       "      <td>业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松鼠Ai智能学习</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>安阳</td>\n",
       "      <td>文峰区</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>0-20</td>\n",
       "      <td>...</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>智能硬件学习机</td>\n",
       "      <td>教育</td>\n",
       "      <td>全职工作</td>\n",
       "      <td>全职工作,教育,数学/统计相关专业,数据分析/挖掘经验,智能硬件学习机</td>\n",
       "      <td>[全职工作, 教育, 数学/统计相关专业, 数据分析/挖掘经验, 智能硬件学习机]</td>\n",
       "      <td>智能硬件</td>\n",
       "      <td>生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...</td>\n",
       "      <td>经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽山机器人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>随州</td>\n",
       "      <td>曾都区</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>0-20</td>\n",
       "      <td>...</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>计算机相关专业</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>教育相关</td>\n",
       "      <td></td>\n",
       "      <td>教育相关,数学/统计相关专业,数据分析/挖掘经验,计算机相关专业</td>\n",
       "      <td>[教育相关, 数学/统计相关专业, 数据分析/挖掘经验, 计算机相关专业]</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>员工旅游，包住，免费工装，团建聚餐，五险一金</td>\n",
       "      <td>深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦可思数据科技</td>\n",
       "      <td>数据分析师（校招）</td>\n",
       "      <td>成都</td>\n",
       "      <td>双流区</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>在校/应届</td>\n",
       "      <td>本科</td>\n",
       "      <td>100-499</td>\n",
       "      <td>...</td>\n",
       "      <td>机器学习建模经验</td>\n",
       "      <td>R</td>\n",
       "      <td>SAS</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td>Python</td>\n",
       "      <td>Python,R,SAS,数据分析/挖掘经验,机器学习建模经验</td>\n",
       "      <td>[Python, R, SAS, 数据分析/挖掘经验, 机器学习建模经验]</td>\n",
       "      <td>大数据</td>\n",
       "      <td>带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...</td>\n",
       "      <td>需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>天星教育</td>\n",
       "      <td>数据分析师(J10073)</td>\n",
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       "      <td>郑州</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>1000-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td>SPSS</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Python,SPSS</td>\n",
       "      <td>[Python, SPSS]</td>\n",
       "      <td>新闻/出版</td>\n",
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       "      <td>负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>杭州超级伙伴科技</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>绍兴</td>\n",
       "      <td>柯桥区</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>100-499</td>\n",
       "      <td>...</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>电商/零售行业数据相关经验</td>\n",
       "      <td>数据分析/挖掘经验</td>\n",
       "      <td></td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>数学/统计相关专业,数据分析/挖掘经验,电商/零售行业数据相关经验</td>\n",
       "      <td>[数学/统计相关专业, 数据分析/挖掘经验, 电商/零售行业数据相关经验]</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖</td>\n",
       "      <td>负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>吾执投资</td>\n",
       "      <td>量化数据研究员</td>\n",
       "      <td>上海</td>\n",
       "      <td>浦东新区</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>0-20</td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Python</td>\n",
       "      <td>Python</td>\n",
       "      <td>[Python]</td>\n",
       "      <td>基金</td>\n",
       "      <td>五险一金，零食下午茶，带薪年假，包吃</td>\n",
       "      <td>职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>洲宇</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>昆明</td>\n",
       "      <td>五华区</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>0-20</td>\n",
       "      <td>...</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python</td>\n",
       "      <td>R</td>\n",
       "      <td>数学/统计相关专业</td>\n",
       "      <td>SQL</td>\n",
       "      <td>Python,R,SQL,数学/统计相关专业</td>\n",
       "      <td>[Python, R, SQL, 数学/统计相关专业]</td>\n",
       "      <td>大数据</td>\n",
       "      <td>年终奖，定期体检，五险一金，餐补，带薪年假</td>\n",
       "      <td>能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>西安易升电子有限公司</td>\n",
       "      <td>证券数据分析</td>\n",
       "      <td>西安</td>\n",
       "      <td>雁塔区</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>20-99</td>\n",
       "      <td>...</td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>风控相关经验</td>\n",
       "      <td>证券数据归纳分析</td>\n",
       "      <td></td>\n",
       "      <td>金融行业数据相关经验</td>\n",
       "      <td>证券数据归纳分析,金融行业数据相关经验,风控相关经验</td>\n",
       "      <td>[证券数据归纳分析, 金融行业数据相关经验, 风控相关经验]</td>\n",
       "      <td>电子/半导体/集成电路</td>\n",
       "      <td>NaN</td>\n",
       "      <td>职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>江苏蕲臻堂</td>\n",
       "      <td>电商数据分析师</td>\n",
       "      <td>徐州</td>\n",
       "      <td>云龙区</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>20-99</td>\n",
       "      <td>...</td>\n",
       "      <td>Python</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Python</td>\n",
       "      <td>Python</td>\n",
       "      <td>[Python]</td>\n",
       "      <td>批发/零售</td>\n",
       "      <td>节日福利，五险，零食下午茶，员工旅游</td>\n",
       "      <td>负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4560 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            公司名称             名称  城市    地区       薪水   薪水最小值    薪水最大值   经历要求  \\\n",
       "0           东方优特          数据分析师  北京   海淀区  10000.0  8000.0  12000.0   经验不限   \n",
       "1       松鼠Ai智能学习          数据分析师  安阳   文峰区   5500.0  3000.0   8000.0   经验不限   \n",
       "2          羽山机器人          数据分析师  随州   曾都区   5500.0  3000.0   8000.0   经验不限   \n",
       "3        麦可思数据科技      数据分析师（校招）  成都   双流区   8500.0  7000.0  10000.0  在校/应届   \n",
       "4           天星教育  数据分析师(J10073)  郑州    郑州   7000.0  6000.0   8000.0   经验不限   \n",
       "...          ...            ...  ..   ...      ...     ...      ...    ...   \n",
       "7648    杭州超级伙伴科技          数据分析师  绍兴   柯桥区   7000.0  6000.0   8000.0   经验不限   \n",
       "7652        吾执投资        量化数据研究员  上海  浦东新区  10500.0  8000.0  13000.0   经验不限   \n",
       "7661          洲宇         数据分析专员  昆明   五华区   5000.0  4000.0   6000.0   经验不限   \n",
       "7663  西安易升电子有限公司         证券数据分析  西安   雁塔区   7500.0  5000.0  10000.0   经验不限   \n",
       "7664       江苏蕲臻堂        电商数据分析师  徐州   云龙区   6000.0  5000.0   7000.0   经验不限   \n",
       "\n",
       "     学历要求       公司规模  ...        关键词1           关键词2       关键词3       关键词4  \\\n",
       "0      本科    100-499  ...        数据处理            数据库       数据挖掘              \n",
       "1      本科       0-20  ...   数学/统计相关专业      数据分析/挖掘经验    智能硬件学习机         教育   \n",
       "2      本科       0-20  ...   数学/统计相关专业        计算机相关专业  数据分析/挖掘经验       教育相关   \n",
       "3      本科    100-499  ...    机器学习建模经验              R        SAS  数据分析/挖掘经验   \n",
       "4      本科  1000-9999  ...      Python           SPSS                         \n",
       "...   ...        ...  ...         ...            ...        ...        ...   \n",
       "7648   本科    100-499  ...   数学/统计相关专业  电商/零售行业数据相关经验  数据分析/挖掘经验              \n",
       "7652   本科       0-20  ...      Python                                        \n",
       "7661   本科       0-20  ...         SQL         Python          R  数学/统计相关专业   \n",
       "7663   本科      20-99  ...  金融行业数据相关经验         风控相关经验   证券数据归纳分析              \n",
       "7664   本科      20-99  ...      Python                                        \n",
       "\n",
       "            关键词5                              关键词_all  \\\n",
       "0                                       数据处理,数据库,数据挖掘   \n",
       "1           全职工作  全职工作,教育,数学/统计相关专业,数据分析/挖掘经验,智能硬件学习机   \n",
       "2                    教育相关,数学/统计相关专业,数据分析/挖掘经验,计算机相关专业   \n",
       "3         Python      Python,R,SAS,数据分析/挖掘经验,机器学习建模经验   \n",
       "4                                         Python,SPSS   \n",
       "...          ...                                  ...   \n",
       "7648   数学/统计相关专业    数学/统计相关专业,数据分析/挖掘经验,电商/零售行业数据相关经验   \n",
       "7652      Python                               Python   \n",
       "7661         SQL               Python,R,SQL,数学/统计相关专业   \n",
       "7663  金融行业数据相关经验           证券数据归纳分析,金融行业数据相关经验,风控相关经验   \n",
       "7664      Python                               Python   \n",
       "\n",
       "                                       关键词_list         工作内容  \\\n",
       "0                             [数据处理, 数据库, 数据挖掘]         在线教育   \n",
       "1     [全职工作, 教育, 数学/统计相关专业, 数据分析/挖掘经验, 智能硬件学习机]         智能硬件   \n",
       "2         [教育相关, 数学/统计相关专业, 数据分析/挖掘经验, 计算机相关专业]         人工智能   \n",
       "3         [Python, R, SAS, 数据分析/挖掘经验, 机器学习建模经验]          大数据   \n",
       "4                                [Python, SPSS]        新闻/出版   \n",
       "...                                         ...          ...   \n",
       "7648      [数学/统计相关专业, 数据分析/挖掘经验, 电商/零售行业数据相关经验]         电子商务   \n",
       "7652                                   [Python]           基金   \n",
       "7661                [Python, R, SQL, 数学/统计相关专业]          大数据   \n",
       "7663             [证券数据归纳分析, 金融行业数据相关经验, 风控相关经验]  电子/半导体/集成电路   \n",
       "7664                                   [Python]        批发/零售   \n",
       "\n",
       "                                                     描述  \\\n",
       "0            五险一金，年终奖，加班补助，节日福利，带薪年假，定期体检，餐补，通讯补贴，零食下午茶   \n",
       "1     生日福利，有无线网，免费工装，全勤奖，带薪年假，团建聚餐，工龄奖，五险一金，保底工资，绩效奖...   \n",
       "2                                员工旅游，包住，免费工装，团建聚餐，五险一金   \n",
       "3     带薪年假，零食下午茶，节日福利，定期体检，年终奖，绩效奖金，餐补，五险一金，交通补助，生日福...   \n",
       "4     零食下午茶，保底工资，绩效奖金，学习与教育，餐补，加班补助，带薪年假，节假日加班费，工龄奖，...   \n",
       "...                                                 ...   \n",
       "7648  五险一金，节日福利，零食下午茶，加班补助，带薪年假，绩效奖金，保底工资，员工旅游，生日福利，年终奖   \n",
       "7652                                 五险一金，零食下午茶，带薪年假，包吃   \n",
       "7661                              年终奖，定期体检，五险一金，餐补，带薪年假   \n",
       "7663                                                NaN   \n",
       "7664                                 节日福利，五险，零食下午茶，员工旅游   \n",
       "\n",
       "                                                     要求  \n",
       "0     业务 需求 完成 相关 数据挖掘 数据分析 占 比 40% 业务 需求 撰写 数据分析 报告...  \n",
       "1     经过 公司 培训 通过 学生 系统 中 学习 产生 数据 报告 学生 下 一步 学习 做好 ...  \n",
       "2     深入 了解 智能 学习 系统 后台 监测 学生 学习 数据 学生 日 报告 周 数据 月 数...  \n",
       "3     需要 深入 了解 高等教育 领域 客户 需求 挑战 明确 业务 场景 设计 分析 方案 整合...  \n",
       "4     负责 省区 当年 当期 销售 数据 统计 分析 负责 省区 代理商 评估 销货 指导 负责 ...  \n",
       "...                                                 ...  \n",
       "7648  负责 山东省 电力 交易 模型 编制 编制 年度 月 度 现货 交易 策略 分析 报告 执行...  \n",
       "7652  职位   具备 良好 心理素质 处事 严谨 细心   熟练 运用 Excel 或者 R 统计...  \n",
       "7661  能 较 理解 需求 完成 日常 任务 耐心 具备 较强 沟通 协调 能力 乐观 积极 较 强...  \n",
       "7663  职位 描述 完成 具体 指定 数据 统计分析 数据 指标 进行 解读 深入分析 负责 数据 ...  \n",
       "7664  负责 公司 运营 指标体系 搭建 完善 通过 业务 敏锐 洞察 理解 构建 数据分析 指标体...  \n",
       "\n",
       "[4560 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=df4.copy()\n",
    "# 2. 重命名列标题\n",
    "df5.rename(columns={'标题': '公司名称'}, inplace=True)\n",
    "\n",
    "\n",
    "# 3. 重新排列列的顺序\n",
    "# 首先，我们需要定义新的列顺序\n",
    "new_order = [\n",
    "    '公司名称', '名称', '城市', '地区', '薪水', '薪水最小值', '薪水最大值', '经历要求', '学历要求',\n",
    "    '公司规模', '公司规模2', '关键词1', '关键词2', '关键词3', \n",
    "    '关键词4', '关键词5', '关键词_all', '关键词_list', '工作内容','描述', '要求'\n",
    "]\n",
    "\n",
    "# 然后，根据新顺序重新排列列\n",
    "df5 = df5[new_order]\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1109d9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df5.to_excel('clean_1')\n",
    "# 将df5保存为Excel文件，这里指定文件名、sheet名等参数\n",
    "df5.to_excel('processed_data.xlsx', sheet_name='Sheet1', index=False)"
   ]
  }
 ],
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    "name": "ipython",
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